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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
PERFORMANCE EVALUATION OF BRAIN TUMOR
DIAGNOSIS TECHNIQUES IN MRI IMAGES
V.Kala1, Dr.K.Kavitha2
1
M.Phil Scholar, Computer Science, Mother Teresa women’s university, (India)
2
Assistant Professor, Dept. of. Computer Science, Mother Teresa women’s university, (India)
ABSTRACT
Magnetic Resonance Imaging (MRI) images are generally employed in ischemic stroke diagnosis since it is
quicker accomplishment and compatibility with majority life affirms devices. This paper proposed an efficient
approach for automated ischemic stroke detection of employing segmentation, feature extraction, median
filtering and classification that distinguish the region of ischemic stroke from sizeable tissues in MR images.
The proposed approach comprises of five stages such as pre-processing, segmentation, median filtering,
features extraction and classification. The former ischemic stroke detection is presented to enhance accuracy
and efficiency of clinical pattern. The experimental results are numerically evaluated by a human proficient.
The average overlap utility, average accuracy and average retrieve between the results found employing our
proposed scheme.
Key Words: Classification And Ischemic Stroke, Feature Extraction, Median Filtering ,MRI
Images, Segmentation
I.INTRODUCTION
Generally, a stroke is denoted as a Cerebrovascular Accident (CVA) which is the frequent loss of the function of
brain owing to interference in the blood issue to the brain. Such can be because of the ischemia induced by
blockage or a bleedings a solution, the impressed brain area cannot purpose that may effect in an unfitness to
precede one or more branches on one slope of the human body, unfitness to realize or articulate speech, or an
unfitness to assure one slope of the visual area.
An ischemic stroke is sometimes addressed in a hospital with thrombolytic agent who is also known as ‘clot
buster’ and approximately hemorrhagic strokes gain from neurosurgery. Since the mean life span of human has
enhanced, stroke gets turn the third extending campaign of death globally behind heart disease and also cancer.
Hazard components for stroke admit geezer hood, cigarette smoking, eminent cholesterol, high blood pressure,
former stroke or Transient Ischemic Attack (TIA), a test fibrillation, and diabetes. Our proposed scheme is an
approach for the detection of stroke to settle the small area of ischemia for a distinct diagnosis and to assort
whether the MR image is getting stroke or not.
II. RELATED WORK
The precision of the brain standardization approach immediately impacts the precision of statistical investigation
of operational Magnetic Resonance Imaging (MRI) information. The medical secular lobe and cortical stratum
structures necessitate an exact enrolment approach owing to prominent bury subject variance. Innovation of
fully automated MRI post treating pipeline directed to minimize the error at the process of registration
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
throughout group analyzes and we will establish their transcendence over two generally employed registration
approaches by leading comprehensive surface to surface length quantifications throughout blunder cortical and
sub cortical areas.
Areas in 3-D Magnetic Resonance Images (MRI) of brain can be assorted utilizing protocols for manually
sectioning and marking structures. For prominent cohorts, expertness and time essentials build such approach
visionary. In order to attain mechanization, a single segmentation can be disseminated to some other single
employing an anatomical reference symmetry approximation linking the atlas image to the objective image. The
precision of the leading target marking has been determined but can possibly be enhanced by aggregating
multiple segmentations employing decision fusion process.
Though researches have furnished abundant manifest for caused advances in psychological and physiological
welfare, trivial is recognized about potential links to brain structure of pattern. Applying high-resolution
Magnetic Resonance Images of 22 Tai Chi Chuan (TCC) practicing and 18 assures checked for age, education
and sex. We take off to analyze the fundamental anatomical correlatives of semi-permanent Tai chi pattern at
two dissimilar levels of regional particularity.
The structure of mean examples of anatomy, besides retrogression investigation of anatomical constructions is
fundamental issues in medical field research, for example in the analysis of brain growth and disease procession.
While the fundamental anatomical operation can be patterned by arguments in a Euclidian space, authoritative
statistical approaches are applicable. Recent epoch work proposes that efforts to depict anatomical reference
divergences employing flat Euclidian spaces counteract our power to constitute natural biological variance.
All areas of neuroscience which utilize medical imaging of brain require for transmitting their solutions with
address to anatomical areas. Particularly, relative morph metric and the group investigation of operational and
physiologic data necessitate brains co-registration to demonstrate agreements throughout brain structures. It is
considerably demonstrated that additive registration of one brain image to another image is unequal for
adjusting brain structures, so legion algorithms induce issued to nonlinearly register brain images to each other.
III. PROPOSED SYSTEM
In our proposed approach we defeat the trouble and withdraw of existing approach. There are five stages are
applied in our process. At feature extraction using GLCM and HOG (Histogram of Gradient) is employed and
for assortment SVM and Neuro fuzzy ANFIS also presented. From the above process, we decide the region of
ischemic stroke in MR images. MR images are more approachable, less costly and faster particularly in
critically ill patients. By using our proposed approach we can obtain high accuracy images and the overall
efficiency of the system is enhanced.
3.1. Data Flow
Our proposed approach contains following four stages
·
Pre-processing
·
Segmentation
·
Feature extraction
·
Classification
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
3.2. Pre-Processing
In pre-processing approach median filters are employed to eliminate noise from the MRI input images. It is
frequently suitable to be capable of executing few kind of noise diminution on images or signals. The median
filters are known as nonlinear digital filters, frequently utilized to eliminate noise. Such noise elimination is a
distinctive pre-processing level to enhance the solutions of more recent processing. Median filtering is widely
utilized in digital image processing approach.
3.3. Architecture Diagram
3.4. Median Filtering
As we have encountered that smoothing filters decrease noise. Nevertheless, the fundamental presumption is
that the adjacent pixels represent extra samples of the like measures as the source pixel that is they constitute the
same characteristic. At the image edges, this is obviously not true, and blurring of characteristics effects. We
have employed convolution approach to enforce weighting kernels as a locality function that presented a linear
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
procedure. There are nonlinear locality functions which can be executed for the intention of noise removal
which can execute a better task of maintaining edges than Simple Smoothing Filters.
3.5. Segmentation
Here, we really extract impressed region from the input image which a part of it and that comprises exactly the
postcode. The aim of segmentation is to vary and simplify the cooperation of an input image into something
which is more significant and lighter to analyze.
3.6. Feature Extraction (GLCM)
Here, we are going to extract the video feature by GLCM and a gray level coincidence matrix (GLCM)
comprises information concerning the situations of pixels causing similar gray level measures. Then, we
compute various movement features at each and every point with local secular units separated in order to regard
straight of motions. We compute the fluctuation between the each and every frame. Such measures will be
employed as feature measures of video.
The GLCM is determined by, Where ‘nij’ is defined as the number of occurrences and that possess the pixel
values ‘(i,j)’ resting at the distance ‘d’ in the input image. The above co-occurrence matrix ‘Pd’ contains the
dimension of about ‘n× n’, where ‘n’ is denoted as the number of gray levels in the input image.
3.7. Classification
In this section, we are going to classify the input image whether the image is frontal or non-frontal image by
employing Support Vector Machine (SVM) classifier. SVMs are also known as support vector networks that are
monitored discovering examples with related discovering algorithms which analyze information and distinguish
patterns, employed for the regression analysis and classification process.
3.8. Svm Classifier
·
Data setup: our proposed dataset comprises three categories, each ‘N’ samples. The information is‘2D’ plot
source information for visual review.
·
SVM with analogy kernel (-t 0) and we require to discover the better parameter measure C employing 2fold cross establishment.
·
After detecting the better parameter measure for C, we aim the full data again employing such parameter
measure.
·
Plot support vectors
·
Plot decision area
SVM functions input vectors to a more eminent dimensional space vector where an optimum hyper plane is
fabricated. Among the various hyper planes uncommitted, there is only too hyper plane which increases the
length between them self and the closest data vectors of each and every class. Such hyper plane that increases
the margin is known as the optimal distinguishing hyper plane. The margin is determined as the addition of
hyper plane distances to the nearest training vectors of each and every category.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
IV. EXPERIMENTAL RESULTS
Histogram of Oriented Gradients appropriates edge or gradient constructions which are feature of local shape.
HOG is an image descriptor which is established on the image’s gradient preferences. Here we extract the
mathematical measure from HOG only; HOG descriptor is established on dominant image edge orientations and
the image are splitted into cells.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
V. CONCLUSION
A Computer Aided Detection (CAD) system is capable of describing few ischemic stroke has been formulated.
We have formulated an automated approach for the ischemic stroke detection in brain of MR images employing
segmentation, feature extraction and classification. Here we enhance the precision by applying classification and
by using SVM to discover the stoke level in the brain image but which is deficient in detecting precision, rather
this we may employ some other progress classifier like GMM, HMM, Feed Forward Neural Network and thus
the accuracy of the input image can be enhanced.
REFERENCE
[1]. Akansha Singh , Krishna Kant Singh, “A Study Of Image Segmentation Algorithms For Different Types
Of Images”, International Journal of Computer Science Issues, vol. 7,Issue 5, pp 414-417,2010.
[2]. Mansur Rozmin, Prof. Chhaya Suratwala, Prof. Vandana Shah,”Implementation Of Hard C-Means
Clustering Algorithm For Medical Image Segmentation”, Journal Of Information Knowledge and
Research in Electronics and Communication Engineering,vol.2, no.2, pp 436-440,Nov12-Oct13.
[3]. Rajesh Kumar Rai, Trimbak R. Sontakke, “Implementation of Image Denoising using Thresholding
Techniques”,International
Journal
of
Computer
Technology
and
Electronics
Engineering
(IJCTEE),vol.1,no. 2, pp 6-10.
[4]. T.Kalaiselvi, S.Vijayalakshmi, K.Somasundara, “Segmentation of Brain Portion from MRI of Head Scans
Using Kmeans Cluster”, International Journal of Computational Intelligence and Informatics ,vol. 1,no. 1,
pp 75-79,2011.
[5]. S.S Mankikar , “A Novel Hybrid Approach Using Kmeans Clustering and Threshold filter For Brain Tumor
Detection”, International Journal of Computer Trends and Technology, vol. 4, no.3, pp 206-209,2013.
[6]. M.C. Jobin Christ, R.M.S.Parvathi, “Segmentation of Medical Image using Clustering and Watershed
Algorithms”, American Journal of Applied Sciences, vol. 8, pp 1349-1352, 2011.
[7]. Manali Patil, Mrs.Prachi Kshirsagar, Samata Prabhu, Sonal Patil, Sunilka Patil,” Brain Tumor Identification
Using K-Means Clustering”, International Journal of Engineering Trends and Technology,vol. 4,no. 3,pp
354-357,2013.
[8]. P.Dhanalakshmi , T.Kanimozhi, “Automatic Segmentation of Brain Tumor using K-Means Clustering and
its Area Calculation”, International Journal of Advanced Electrical and Electronics Engineering ,vol. 2,no.
2,pp 130-134,2013.
[9]. Sanjay Kumar Dubey, Soumi Ghosh, “Comparative Analysis of K-Means and Fuzzy C Means Algorithms”,
International Journal of Advanced Computer Science and Applications, vol. 4, no. 4, pp 35-39, 2013.
[10]. M. Masroor Ahmed, Dzulkifli Bin Mohamad, “Segmentation of Brain MR Images for Tumor Extraction
by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model”, International Journal
of Image Processing, vol. 2 , no. 1, pp 27-34,2008.
[11]. Anam Mustaqeem, Ali Javed, Tehseen Fatima, “An Efficient Brain Tumor Detection Algorithm Using
Watershed & Thresholding Based Segmentation”, I.J. Image, Graphics and Signal Processing, vol. 10,no.
5, pp 34-39,2012.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
[12]. P.Vasuda, S.Satheesh, “Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation”,
International Journal on Computer Science and Engineering (IJCSE), vol. 02, no.05, pp 1713-1715, 2010.
[13]. Ananda Resmi S, Tessamma Thomas, ”Automatic Segmentation Framework for Primary Tumors from
Brain MRIs Using Morphological Filtering Techniques”, in 5th Int Conf on Biomedical Engineering and
Informatics,2012,IEEE.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
A REVIEW OF BENCHMARKING IN SERVICE
INDUSTRIES
Sunil Mehra1, Bhupender Singh2, Vikram Singh3
1
M.Tech. Student, 2Asst. Professor, 3Associate Professor, Mechanical Engineering,
YMCA University of Science and Technology, (India)
ABSTRACT
Benchmarking is recognized as a fundamental tool for continuous improvement of quality. The objective of this
paper is to examine service factor that contribute to the effectiveness of benchmarking in service industries. This
study found that complexity and flexibility has significant correlation with effectiveness of benchmarking in
service industries. The purpose of this paper is to understand of success factors aimed at increasing service
revenue in manufacturing companies or service industries. We find out different types of success factors and
those factors are improving performance of any type of service industries.
Keywords: Benchmarking, Finding Factors, Implementing Benchmarking, Organisation, Service
I. INTRODUCTION
Benchmarking is one of the most useful tools of transferring knowledge and improvement into organisations as
well as industries (Spendolini, 1992; Czuchry et al., 1995). Benchmarking is used to compare performance with
other organisations and other sectors. This is possible because many business processes are basically the same
from sector to sector. Benchmarking focused on the improvement of any given business process by exploiting
best rather than simply measuring the best performance. Best practices are the cause of best performance.
Companies studying best practices have the greatest opportunity for gaining strategic, operational, and financial
advantages. Benchmarking of business processes is usually done with top performing companies in other
industry sectors.
The systematic discipline of benchmarking is focused on identifying, studying, analysing, and adapting best
practices and implementing the results. The benchmarking process involves comparing one’s firm performance
on a set of measurable parameters of strategic importance against those firms known to have achieved best
performance on those indicators. Development of benchmarking is an iterative and ongoing process that is likely
to involve sharing information with other organisations working with them towards a satisfying metrology.
Benchmarking should be looked upon as a tool for improvement within a wider scope of customer focused
improvement activities and should be driven by customer and internal organisation needs. Benchmarking is the
practices of being humble enough to admit that someone else is better at something and wise enough to learn
how to match even surpass them at it.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
II. IMPLEMENTATION OF BENCHMARKING
There are five phases for implementation of benchmarking
2.1 Planning
Planning is the first step of implementation of benchmarking in any organisation. During this phase the
organisation determines which process to benchmark and against what type of organisation.
2.2 Analysis
During this phase to analysis is performed for the performance gap between the source organisation and the
receiver organisation.
2.3 Integration
It involves the preparation of the receiver for implementation of actions.
2.4 Action
This is the phase where the actions are implemented within the receiver organization.
2.5 Maturity
This involves continuous monitoring of the process and enables continuous learning and provides input for
continuous improvement within the receiver organization.
III. SERVICE
The concept of service can be described as the transformation of value, an indescribable product, from the
service supplier (also termed the provider) to the customer (also termed the consumer). The process of
transformation can be set in motion by a customer whose needs can be provided by the supplier, by a service
supplier who offers a particular service to the customer.
The definitions usually include:
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
·
Public transportation.
·
Public utilities (telephone communication, energy service, sanitation stores).
·
Restaurants, hotels and motels.
·
Marketing (retail food, apparel, automotive, wholesale trade, department stores).
·
Finance (commercial banks, insurance, sales finance, investment).
·
Personal service (amusements, laundry and cleaning, barber and beauty shops).
·
Professional services (physicians, lawyers).
·
Government (defense, health, education, welfare, municipal services).
·
News media
IV. LITERATURE REVIEW
4.1 Roger Moser et al., (2011),
In order to develop a benchmarking framework for supply network configuration we draw upon insights various
theories addressing different levels: the dyadic relationship, the supply chain, and the network levels. The all
three levels for the benchmarking of supply network configuration. The foundation for the development of our
benchmarking framework for supply network configuration is primarily based on the theories of relationships
and networks.
4.2 Heiko Gebauer et al., (2011),
The different requirements of the service strategies described as “after-sales service providers” and “customer
support service providers” influence the logistics of spare parts. The service organisations would face increasing
pressure to improve their financial performance, and compared with the corresponding values for the
manufacture and distribution of the finished product within the company.
4.3 Min et al., (Hokey 2011)
The benchmarking process begins with the establishment of service standards through identification of service
attributes that comprise service standards. Since serving customer better is the ultimate goal of benchmarking.
4.5 Panchapakesan padma et al., (2010)
Several researchers have established that service quality not only influences the satisfaction of buyers but also
their purchase intentions. Even though there are other antecedents to customer satisfaction , namely, price,
situation, and personality of the buyer, service quality receives special attention from the service marketers
because it is within the control of the service provider and by improving service quality its consequence
customer satisfaction could be improved, which may be turn influence the buyer’s intention to purchase the
service.
4.5 Luiz Felipe Scavard et al., (2009)
Product variety proliferation is a trend in many industry sectors worldwide. Benchmarking should be a reference
or measurement standard for comparison a performance measurement that is the standard of excellence for a
specific business and a measurable, best in class achievement. Benchmarking as the continuous process of
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
measuring products, services, and practices against the toughest competitors or those organization known as
industry leaders.
4.6 Monika koller, Thomas Salzberger, (2009)
Benchmarking is a tool which is widely used in both the manufacturing as well as the service industry. When we
compared to the manufacturing sector, benchmarking in the service sector is more difficult because
idiosyncrasies of particular fields of application. Bhutta and Huq (1999) gives that benchmarking is not only a
comparative analysis, it is a process to establish the ground for creative breakthroughs and a way to move away
from tradition.
4.7 Okke Braadbaart, (2007),
Benchmarking has significant potential for application in the public sector. According to Magd and Curry
(2003), benchmarking is a powerful vehicle for quality improvement and a paradigm for effectively managing
the transformation of public-sector organizations into public-sector organizations of quality. The collaborative
benchmarking literature focuses on information sharing and the positive effect this has on the quality and
quantity of information about what public sector organisations do and how well they do it.
4.8 Sameer Prasad, Tata, (2006)
Employing an appropriate theory building process can help us improve our precision and understanding. Both
precision and understanding are important element of benchmarking. The greater the degree of precision allows
for a finer ability to make comparisons. Greater power provides an understanding on the way to improve upon
the service.
4.9 Nancy M. Levenburg, (2006)
Benchmarking is the process of comparing one’s practices and procedures against those believed to be the best
in the industry. While benchmarking efforts focused on manufacturing and logistics, the process has grown to
encompass a wider array of activities including exporting, quality goals in service systems, supply chain
interface, employee practices and brand management. This study aims to gain insight into practice that can
enable organizations to utilize the internet more effectively for customer service purpose.
4.10 Ashok Kumar et al., (2006)
Benchmarking has been variously defined as the process of identifying, understanding, and adapting outstanding
practices from organizations anywhere in the world to help your organizations improve its performance. It is an
activity that looks outward to find best practice and high performance and then measures actual business
operations against those goals.
V. FINDING FACTORS
With the help of this literature review we find out different types of success factors and those factors are
improving performance of any type of service industries. Many service factors are finding in service industries.
Some factors are also inter relate to each other. Service factor are:-
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
Sr.
ISBN: 978-81-931039-3-7
Factor
Author
Define
Unique or superior
Melton and Hartline,
Offering superior core attributes and supporting
service
2010
services.
Synergy
Ottenbacher
No.
1
2
and
Capabilities
Harrington, 2010
3
customer
Carbonell et al., 2009
involvement
Operational outcomes and innovation volume, but
no impact on competitive superiority and sales
performance.
4
Technology
Lin et al., 2010
By applying more advanced marketing information
systems based on the data acquired from their
customers, companies are able to create more
service innovations to explore potential markets
5
6
Knowledge
Leiponen,
Collective ownership of knowledge should be
management
2006
promoted.
Culture
Liu (2009)
Supportive culture as a construct of complementary
dimensions consisting of innovative
7
Market orientation
Atuahene-Gima (1996)
The organization-wide collection
and dissemination of market information, as well as
the
organizational
responsiveness
to
that
information.
8
Process quality
Avlonitis et al., 2001
This factor is important in all phases from idea
generation and analysis to concept development,
testing, and launch.
9
Cross-functional
Storey et al.,2010
It seems to be important in companies which rely
involvement
heavily on tacit knowledge, where the codification
of information is difficult
10
employee
Ordanini
involvement
Parasuraman, 2011,
and
Extensive internal marketing is conducted to raise
support and enthusiasm for the product
VI. CONCLUSION
From this review it is seen that Benchmarking is an important tool to improve quality of the product/service in
manufacturing as well as service industries. The main objective of the review to highlight the main factor in
service industries and these are interrelate each other. These different types of successive factor are improving
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
performance of any type service industries as well as manufacturing and those factors are also useful for
implementation of benchmarking. A service entails a unique experience between the service provider and
service customer. The constellation of features and characteristics inherent in a service offering takes place
during its development. Hence, it is important to be aware of certain elements which contribute to the success of
a service while designing it. Benchmarking establishes company’s true position versus the rest, making thus
easier for the company to raise organizational energy for change and develop plans for action.
REFERENCES
[1]
Heiko Gebauer, Gunther Kucza, Chunzhi Wang, (2011),"Spare parts logistics for the Chinese market",
Benchmarking: An International Journal, Vol. 18 Iss: 6 pp. 748 – 768.
[2]
Roger Moser, Daniel Kern, Sina Wohlfarth, Evi Hartmann, (2011),"Supply network configuration
benchmarking: Framework development and application in the Indian automotive industry",
Benchmarking: An International Journal, Vol. 18 Iss: 6 pp. 783 – 801.
[3]
Sameer Prasad, Jasmine Tata, (2006),"A framework for information services: benchmarking for countries
and companies", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 311 – 323.
[4]
Nancy M. Levenburg, (2006),"Benchmarking customer service on the internet: best practices from family
businesses", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 355 – 373
[5]
Monika Koller, Thomas Salzberger, (2009),"Benchmarking in service marketing - a longitudinal analysis
of the customer", Benchmarking: An International Journal, Vol. 16 Iss: 3 pp. 401 – 414.
[6]
Okke Braadbaart, (2007),"Collaborative benchmarking, transparency and performance: Evidence from
The Netherlands water supply industry", Benchmarking: An International Journal, Vol. 14 Iss: 6 pp. 677 –
692.
[7]
Ashok Kumar, Jiju Antony, Tej S. Dhakar, (2006),"Integrating quality function deployment and
benchmarking to achieve greater profitability", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp.
290 – 310. for achieving service innovation. Decision Sciences, 40, 431-475.
[8]
Luiz Felipe Scavarda, Jens Schaffer, Annibal José Scavarda, Augusto da Cunha Reis, Heinrich Schleich,
(2009),"Product variety: an auto industry analysis and a benchmarking study", Benchmarking: An
International Journal, Vol. 16 Iss: 3 pp. 387 – 400.
[9]
Panchapakesan Padma, Chandrasekharan Rajendran, Prakash Sai Lokachari, (2010),"Service quality and
its impact on customer satisfaction in Indian hospitals: Perspectives of patients and their attendants",
Benchmarking: An International Journal, Vol. 17 Iss: 6 pp. 807 – 841
[10] Hokey Min, Hyesung Min, (2011),"Benchmarking the service quality of fast-food restaurant franchises in
the USA: A longitudinal study", Benchmarking: An International Journal, Vol. 18 Iss: 2 pp. 282 – 300.
[11] G.M. Rynja, D.C. Moy, (2006),"Laboratory service evaluation: laboratory product model and the supply
chain", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 324 – 336.
[12] Heiko Gebauer, Thomas Friedli, Elgar Fleisch, (2006),"Success factors for achieving high service
revenues in manufacturing companies", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 374 –
386.
[13] Jo Ann M. Duffy, James A. Fitzsimmons, Nikhil Jain, (2006),"Identifying and studying "best-performing"
services: An application of DEA to long-term care", Benchmarking: An International Journal, Vol. 13 Iss:
3 pp. 232 – 251.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
[14] Li-Jen Jessica Hwang, Andrew Lockwood, (2006),"Understanding the challenges of implementing best
practices in hospitality and tourism SMEs", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp.
337 – 354.
[15] Lin Peter Wei-Shong, Mei Albert Kuo-Chung, (2006),"The internal performance measures of bank
lending: a value-added approach", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 272 – 289.
[16] Mahmoud M. Nourayi, (2006),"Profitability in professional sports and benchmarking: the case of NBA
franchises", Benchmarking: An International Journal, Vol. 13 Iss: 3 pp. 252– 271.
[17] Sarath Delpachitra, (2008),"Activity-based costing and process benchmarking: An application to general
insurance", Benchmarking: An International Journal, Vol. 15 Iss: 2 pp. 137 – 147.
[18] Hervani, A.A., Helms, M.M. and Sarkis, J. (2005), “Performance measurement for green supply chain
management”, Benchmarking: An International Journal, Vol. 12 No. 4, pp. 330-53.
[19] Kyro¨, P. (2003), “Revising the concept of benchmarking”, Benchmarking: An International Journal, Vol.
10 No. 3, pp. 210-25.
[20] Simatupang, T.M. and Sridharan, R. (2004), “Benchmarking supply chain collaboration, an empirical
study”, Benchmarking: An International Journal, Vol. 11 No. 5, pp. 484-503.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
IDENTIFICATION OF BARRIERS AFFECTING
IMPLEMENTATION OF 5S
Sunil Mehra1, Rajesh attri2, Bhupender Singh3
1
M.Tech. Student, 2,3Asst. Professor, Mechanical Engineering,
YMCA University of Science and Technology, (India)
ABSTRACT
The 5S methodology is the best tool for generating a change in attitude among employee and serves as a way to
engage important activities the workplace. But, implementation of this universally accepted and challenging
system is not an easy task by any means as it requires establishing new cultures, changing attitudes, creating
good work environments. There are certain barriers which effects the implementation of 5S in the
manufacturing organisation. The aim of the present work is to identify the different types of barriers which
affect the implementation of 5S in manufacturing organisation.
Keywords: 5s, Barriers, Implementation, Manufacturing Organisation
I. INTRODUCTION
The 5S methodology is a very suitable way to start the process of continuous improvement (Carmen Jaca et al.,
2013). The 5S methodology is one of the best tools for generate a change in attitude among workers and serves
as a way to engage improvement activities the workplace (Gapp et al., 2008). This methodology was developed
in Japan by Hirano (1996). The 5S name corresponding to the first latter of five Japanese word – Seiri, Seiton,
Seiso, Seiketsu, Shitsuke, (Ramos Alonso 2002) and their rough English equivalents – Sort, Set in order
(Straighten), Shine, Standardize, Sustain. Hirano establishes that Lean Culture requires a change in people’s
mentality as well as applying 5S as a requirement for the implementation of other actions to achieve
improvement and as a basic step towards eliminating waste. In Japanese culture each word that makes up 5S
means the following (Ramos Alonso 2002):
1. Seiri (Sort) – In which, we identify what is needed to do daily work, what is not needed, and what can be
improved.
2. Seiton (Straighten/ Set in Order) – In which, we organise the work area with the best locations for the
needed items.
3. Seiso (Shine) – In which, we Clean or remove reasons for unorganized, unproductive and unsafe work.
Create measures and preventative maintenance to ensure the Shine step.
4. Seiketsu (Standardize) – In which, we provide procedures to ensure understanding of the process. This S
supports the first 3 S’. Keep using best practices.
5. Shitsuke (Sustain) – In which, we set up the system to ensure the integrity of the process and build it so it
that improvement is continuous.
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5S can ultimately be applied to any work area, in and outside manufacturing. The same techniques apply to any
process including those in the office (Liker et al., 2004).
The main objective of the current research is to identify the barriers in the implementation of 5S in Indian
manufacturing organizations.
II. BENEFITS OF 5S
•
5S will improve work efficiency, safety and also improves productivity.
•
A clean and organised workplace is safer. It decreases the possibility of injuries occurring.
•
Increase product quality and process quality.
•
5S will motivate and involve your employees.
•
5S will organize your whole organisation as well as workplace.
•
5S will remove clutter from workspace.
III. IMPLEMENTATION OF 5S
The 5S implementation requires commitment from both top management and everyone in the organisation. The
5S practice requires investment in time and if properly implemented it has a huge impact on organisational
performance (Ho 1999a; linker 2004; linker and Hoseus 2008).
The effective implementation of the 5S method is the responsibility of the management and the entire team of
employees. The implementation should be carried out after prior training and making staff aware of the validity
and the effectiveness of the method used.
Literature review and experiences of managers and academicians reveal that implementation of 5S is not an easy
task by means as it requires establishing new cultures, changing attitudes, creating good work environment’s
and shifting the responsibility to the every employee of the organisation. The main purpose of implementing 5S
is to achieve better quality and safety.
IV. IDENTIFICATION OF BARRIERS
4.1 Lack of Top Management Commitment
Top management is to control and help the continuous improvement activities, it relate the activities to business
target and goals. Top management support and commitment is necessary for any strategic program success
(Hamel & Prahalad, 1989; Zhu & Sarkis, 2007). Lack of commitment from top management is a chief barrier
for successful adoption of green business practices (Mudgal et al., 2010). Without top management
commitment, no quality initiative can succeed. Top management must be convinced that registration and
certification will enable the organization to demonstrate to its customers a visible commitment to quality. The
top management should provide evidence of its commitment to the development and implementation of the
quality management system and continually improve its effectiveness. The lack of management support is
attributed to management not completely understanding the true goal of the implementation of 5S (Chan et al.,
2005; Rodrigues and Hatakeyama, 2006 and Attri et al., 2012).
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4.2 Financial Constraints
Financial constraints are a key barrier to implementation of 5S. Information and technological systems require
more funds because without these, implementation of 5S is not possible in the present environment. Funds are
needed to institute training programs, provide quality resources, payments for external consultants, payment for
auditors, and payment for certification. Lack of financial support affects the certification programs also. 5S
improves operational efficiency, customer service, provides an ability to focus on core business objectives and
provides greater flexibility (Barve et al., 2007). If any organization has insufficient financial resource, it will not
be in their budget to implementation of 5S.
4.3 Lack of Awareness of 5S
A major barrier of 5S seen in Indian organisation is lack of awareness about the benefits of the 5S provider. The
lack of awareness of the benefits of the 5S both from economic and agile could be a major factor for the
resistance to change to 5S. But in manufacturing organisation, due to lack of awareness of 5S, manufacturing
organisation is not able to improve performance and work efficiency. If employees of any industry will not have
proper understanding of 5S. They will not achieve their objective and goals. Better understanding will help in
implementation of 5S. Thus, we can say that lack of awareness of 5S is a major barrier to implementation of 5S.
4.4 Lack of Strategic Planning of 5S
Strategic planning is the identification of 5S goals and the specification of the long term plan for managing
them. The main role of strategic planning is of paramount importance to any new concept to get institutionalized
and incorporated into routine business.
4.5 Lack of Employee Commitment
Employee check the deadlines and result of the continuous improvement activities, it spend time helping root
cause solving problem activities and standardization. Employee has good communication skills and sufficient
knowledge about the implementation of 5S. Employee has confidence on 5S implementation and its result.
Without employee commitment, no quality initiative can succeed.
4.6 Resistance to Change and Adoption
To attenuate this resistance to change, employees at all levels of the organization must be educated about the
goals of 5S implementation well in time (Khurana, 2007). A chief barrier seen in implementation of 5S is the
resistance to change, human nature being a fundamental barrier. Employee’s commitment to change
programmes is essential given that they actually execute implementation activities (Hansson et al., 2003).
4.7 Lack of Cooperation/Teamwork
The success of any type of business relies on the effective teamwork and collaboration of employees at all levels
of the organisation. When employees fail to work together as a team, business initiatives and goals become more
difficult to attain and the surroundings workplace environment can become negative and disrupting.
4.8 Lack of Education and Training
Employees of an organization must be properly educated and trained in a sufficient manner. If employees are
not trained, this factor also affects the implementation of 5S. Without proper knowledge they will not be aware
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of work culture of this quality program and many misconceptions will be in their mind without training.
According to Mital et al. (1999), there is a dire need to train workers in manufacturing organizations and thereby
improve the overall effectiveness and efficiency of such organizations. A long-term educational and training
programme should be designed and implemented to train the employees so that reallocation of the human
resource for jobs requiring advanced technical expertise can be made possible (Hung and Chu, 2006).
4.9 Lack of Motivation
Motivation derived from the latin word ‘movere’, which means to move (Kretiner, 1998). It is an inner drive and
intention that causes a person to do something or get a certain way (Fuhrmann, 2006). Employee motivation is a
major factor in the success or failure for any organization (Barrs, 2005). Employees as the bridge to
Competitiveness So Organizations must invest in effective strategies to motivate employees (Latt, 2008).
Motivation changes the behavior of an employee towards work from negative to positive. Besides that, without
motivated employees, profit, products, services, moral and productivity suffer.
4.10 Inability to Change Organisational Culture
The organisational culture provides the rule of behavior and attitude. Organisational culture is also motivating
the employees and helps leaders accelerate strategy implementation in their organisation. Top management is
able to change organisational culture for improving performance and work efficiency.
4.11 Non – Clarity of Organisation Policy and 5S Programme
Managers and employee have non - clarity of organisation policy and objectives of 5S. Managers have sufficient
technical knowledge and skills to manage their employees and clarify organisation policy for achieving their
goals and objective.
4.12 Lack of Communication
Employees are not communicating about the continuous improvement result, the activities being under taken,
the people that was part of the activities, the objectives and next steps. Communication is very essential in any
organization. One department has to communicate to the other to get some information. So the relations between
the departments should be good otherwise it will harm the effectiveness of the organization. Lack of
communication will also result in the non- participation of the employee. An effective communication channel
is required in the organization.
4.13 No Proper Vision and Mission
For good implementation of 5S any organization must have a proper vision and mission. In any type of an
organization without an aim will not be able to take advantage of the quality program. An organization should
be clear in this aspect that why they are implementing 5S and what are their objectives or goals.
4.14 Lack of Leadership
Leadership relating to quality is the ability to inspire people to make a total willing and voluntary commitment
to accomplishing or exceeding organizational goals. Leadership establishes the unity and purpose for the
internal environment of the organization. 5S may fail due to lack of leadership.
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4.15 Conflict with Other Quality Management System
Quality management system is a technique used to communicate to employees. Each employee has different
opinion regarding the quality management system so there is a conflict among the employee. The rule and
regulation also vary from one quality system to another.
V. CONCLUSION
The present study conclude that 5S is an important tool to organize the whole organization in a systematic
manner. 5S satisfies both organization and customer. Implantation of 5S method is the responsibility of the
management and the entire team of employees. In this study we identify the different barriers that hinder the
implementation of 5S in an organization. With the help of these barriers will improve performance and
efficiency in any type of organization. These barriers are independent to each other so with the help of these
barriers we improve quality and performance.
REFERENCES
[1]. Ahuja, I., & Khamba, J. (2008). Strategies and success factors for overcoming challenges in TPM
implementation in Indian manufacturing industry. Journal of Quality in Maintenance Engineering, 14(2),
123-147.
[2]. Bain, N. (2010). The consultant guide to successfully implementing 5S. Retrieved August, 3, 2010, from
http: www.leanjourney.ca/Preview/Preview- TheConsultantsGuideTo5S.pdf
[3]. Attri, R., N. Dev, and V. Sharma. 2013. “Interpretive Structural Modelling (ISM) Approach: An
Overview.” Research Journal of Management Sciences 2 (2): 3–8.
[4]. Achanga, P., E. Shehab, R. Roy, and G. Nelder. 2006. “Critical Success Factors for Lean Implementation
within SMEs.” Journal of Manufacturing Technology Management 17 (4): 460–471.
[5]. Ablanedo-Rosas, J., B. Alidaee, J. C. Moreno, and J. Urbina. 2010. “Quality Improvement Supported by
the 5S, an Empirical Case Study of Mexican Organisations.” International Journal of Production Research
48 (23): 7063–7087.
[6]. Anand, G., & Kodali, R. (2010). Development of a framework for implementation of lean manufacturing
systems. International Journal of Management Practice, 4(1), 95–116.
[7]. Dahlgaard, J.J., & Dahlgaard-Park, S.M. (2006). Lean production, six sigma quality, TQM and company
culture. The TQM Magazine, 18(3), 263–281.
[8]. Hospital .(2001). Report on the 5s training in provincial hospitals of three convergence sites. Unpublished
report: Negros Oriental Provincial Hospital.
[9]. Kumar, S., & Harms, R. (2004). Improving business processes for increased operational efficiency:a case
study. Journal of Manufacturing Technology Management, 15(7), 662–674.
[10]. Ramlall, S. (2004). A Review of Employee Motivation Theories and their Implications for Employee
Retention within Organizations . The Journal of American Academy of Business, 52-63.
[11]. Raj, T., Shankar, R. and Suhaib, M. (2009) ‘An ISM approach to analyse interaction between barriers of
transition to Flexible Manufacturing System’, Int. J. Manufacturing Technology and Management, Vol.
[12]. Raj T. and Attri R., Identification and modelling of barriers in the implementation of TQM, International
Journal of Productivity and Quality Management, 28(2), 153-179 (2011).
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[13]. Attri R., Grover S., Dev N. and Kumar D., An ISM approach for modelling the enablers in the
implementation of Total Productive Maintenance (TPM), International Journal System Assurance
Engineering and Management, DOI: 10.1007/s13198-012-0088-7 (2012) 16. Attri R., Grover S., Dev N.
and Kumar D., An.
[14]. Ab Rahman, M.N., et al., Implementation of 5S Practices in the Manufacturing Companies: A Case Study.
American Journal of Applied Sciences, 2010. 7(8): p. 1182-1189.
[15]. Hough, R. (2008). 5S implementaion methodology. Management Services, 35(5)44-45. Retrieved from
Academic Search Complete database.
[16]. Gapp, R., R. Fisher, and K. Kobayashi. 2008. “Implementing 5S within a Japanese Context: An Integrated
Management System.” Management Decision 46 (4): 565–579.
[17]. Hirano, H. 1996. 5S for Operators. 5 Pillars of the Visual Workplace. Tokyo: Productivity Press.
[18]. Ramos Alonso, L. O. 2002. “Cultural Impact on Japanese Management. An Approach to the Human
Resources Management.” [La incidencia cultural en el management japonés. Una aproximación a la
gestión de los recursos humanos]. PhD diss., Universidad de Valladolid, Valladolid, Spain.
[19]. Ho, S. K. M. 1998. “5-S Practice: A New Tool for Industrial Management.” Industrial Management &
Data Systems 98 (2): 55–62.
[20]. Liker, J.K., 2004. The Toyota way: fourteen management principles from the world’s greatest
manufacturer. New York: McGraw-Hill.
[21]. Liker, J.K. and Hoseus, M., 2008. Toyota culture: the heart and soul of the Toyota way. New York:
McGraw-Hill.
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VARIOUS WAVELET ALGORITHMS FOR IRIS
RECOGNITION
R.Subha1, Dr. M.Pushparani2
1
Research Scholar, Mother Teresa Women’s University, Kodaikanal
2
Professor and Head, Department of Computer Science,
Mother Teresa Women’s University, Kodaikanal (India)
ABSTRACT
Individual identification has become the need of modern day life. The recognition must be high-speed, automatic
and infallible. Biometrics has emerged as a strong alternative to identify a person compared to the traditional
ways. Also biometric identification can be made speedy, automatic and is already foolproof. Among other
biometrics, Iris recognition has emerged as a strong way of identifying any person. Iris recognition is one of the
newer biometric technologies used for personal identification. It is one of the most reliable and widely used
biometric techniques available. In geAneral, a typical iris recognition method includes Localization,
Normalization, and Matching using traditional and statistical methods. Each method has its own strengths and
limitations. In this paper, we compare the recital of various wavelets for Iris recognition algorithm like complex
wavelet transform, Gabor wavelet, and discrete wavelet transform.
Keywords: Iris Recognition, Complex Wavelets, Gabor Wavelets, Discrete Wavelet Transform
I. INTRODUCTION
Biometric is the science of recognizing a person based on physical or behavioral characteristics. The commonly
used biometric features include speech, fingerprint, face, voice, hand geometry, signature, DNA, Palm, Iris and
retinal identification future biometric identification vein pattern identification, Body odor identification, Ear
shape identification, Body salinity (salt) identification. Out of all biometric system, Iris biometric is best
suitable as it cannot be stolen or cannot be easily morphed by any person. The human iris is an annular part
between the pupil and white sclera has an extraordinary structure.
Fig 1: The Human Eye Structure
The iris begins to form in the third month of gestation and structures creating its pattern are largely complete by
the eight months, although pigment accretion can continue in the first postnatal years. Its complex pattern can
contain many distinctive features such arching ligaments, furrows, ridges, crypts, rings corona, and freckles.
These visible characteristics, which are generally called the texture of the iris, are unique to each subject.
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II. OVERVIEW OF THE IRIS RECOGNITION SYSTEM
Biometrics is automated method of identifying a person or verifying the identity of a person based on
physiological or behavioral characteristic. Examples of physiological characteristics include hand or fingers
images, facial characteristic and iris recognition. Behavioral Image processing techniques can be employed to
extract the unique iris pattern from a digitized image of the eye and encode it into the biometric template,
which can be stored in database.
The iris being a protected internal organ, whose random texture is most reliable and stable throughout life, can
serve as a kind of living password that one need not to remember but always carries along. Every iris is distinct,
even two irises of the same individual, and the irises of twins are different. Iris patterns are formed before birth
and do not change over the course of a life time. This biometric template contains an objective mathematical
representation of the unique information stored in the iris, and allows the comparisons made between
templates. When a person wishes to be identified by an iris recognition system, their eye is first photographed
and then template is created for their iris region. This template is then compared with the template stored in a
database, until either a matching template is found and a subject is identified, or no match is found and subject
remains unidentified.
III. VARIOUS WAVELETS TRANSFORM
3.1 Complex Wavelets
Complex Wavelets Transforms use complex valued filtering that decomposes the real/complex signals into
real and imaginary parts in transform domain. The real and imaginary coefficients are used to compute
amplitude and phase information, just the type of information needed to accurately describe the energy
localization of oscillating functions. Here complex frequency B-spline wavelet is used for iris feature
extraction.
3.1.1 Result
The iris templates are matched using different angles 210,240,280,320 and 350 Degrees and it is observed
that as angles increases percentage of matching also increases the better match is observed at angle 350
which is 93.05%.Further by detecting eyelids and eyelashes the iris image is cropped and iris template is
generated for matching purpose the results obtained is better than previous results the matching score is
95.30%.
3.2 Gabor Wavelet
The main idea of this method is that: firstly we construct two-dimensional Gabor filter, and we take it to
filter these images, and after we get phase information, code it into 2048 bits, i.e. 256 bytes. In image
processing, a Gabor filter, named after Dennis Gabor, is a linear filter used for edge detection. Frequency and
orientation representations of Gabor filter are similar to those of human visual system, and it has been
found to be particularly appropriate for texture representation and discrimination. In the spatial domain, a 2D
Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. The Gabor filters are selfsimilar - all filters can be generated from one mother wavelet by dilation and rotation.
Its impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of
the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's
impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier
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transform of the Gaussian function. The filter has a real and an imaginary component representing
orthogonal directions. The two components may be formed into a complex number or used individually.
Gabor filters are directly related to Gabor wavelets, since they can be designed for a number of dilations and
rotations. However, in general, expansion is not applied for Gabor wavelets, since this requires computation of
bi-orthogonal wavelets, which may be very time- consuming. Therefore, usually, a filter bank consisting of
Gabor filters with various scales and rotations is created. The filters are convolved with the signal,
resulting in a so-called Gabor space. This process is closely related to processes in the primary visual cortex.
Jones and Palmer showed that the real part of the complex Gabor function is a good fit to the
receptive field weight functions found in simple cells in a cat's striate cortex.
3.2.1 Result
We use the Daugman's methods to iris regions segmentation and use Gabor wavelet for feature extraction. At
last, in the identification stage we calculate Hamming distance between a test image & a training image.
The smallest distance among them is expressed, that test image belongs to this class. The recognition rate is
96.5%.
3.3 The Discrete Wavelet Transform
Computing wavelet coefficients at every possible scale is a fair amount of work, and it generates an awful lot of
data. That is why we choose only a subset of scales and positions at which to make our calculations. It turns
out, rather remarkably, that if we choose scales and positions based on powers of two so-called dyadic scales
and positions then our analysis will be much more efficient and just as accurate.
3.3.1 Result
The technique developed here uses all the frequency resolution planes of Discrete Wavelet Transform
(DWT). These frequency planes provide abundant texture information present in an iris at different
resolutions. The accuracy is improved up to 98.98%. With proposed method FAR and FRR is reduced up to
0.0071% and 1.0439% respectively.
IV. CONCLUSION
In this paper, we compare the performance of various wavelets for Iris recognition like complex wavelet
transform, Gabor wavelet, and discrete wavelet transform. Using complex wavelet, different coefficient
vectors are calculated. Minimum distance classifier was used for final matching. The smaller the distance the
more the images matched. It is observed that for the complex wavelets the results obtain are good than the
simple wavelet because in complex wavelet we get both phase and angle also real and imaginary coefficients,
so we can compare all these parameters for iris matching purpose.2D Gabor wavelets have the highest
recognition rate. Because iris is rotator, and 2D Gabor wavelets have rotation invariance, it has the
highest recognition rate. But 2D Gabor wavelets have high computational complexity, and need more
time. Discrete wavelet transform used for iris signature formation gives better and reliable results.
REFERENCE
[1].
Biometrics: Personal Identification in a Networked Society, A. Jain, R. Bolle and S. Pankanti, eds.
Kluwer, 1999.
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[2].
ISBN: 978-81-931039-3-7
D. Zhang, Automated Biometrics: Technologies and Systems. Kluwer, 2000 Anil Jain. Introduction to
Biometrics. Michigan State University, East Lansing, MI.
[3].
L. Ma, T, Yunhong Wang, and D. Zhang. Personal identification based on iris texture analysis. IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol.25, no.12, 2003
[4].
John Daugman. Recognizing persons by their iris patterns. Cambridge University, Cambridge, UK.
[5].
J. Daugman, "Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition," Int'l
J. Wavelets, Multiresolution and Information Processing, vol. 1, no. 1, pp. 1-17, 2003
[6].
W. Boles and B. Boashash, "A Human Identification Technique Using Images of the Iris and Wavelet
Transform," IEEE Trans. Signal Processing, vol. 46, no. 4, pp. 1185- 1188, 1998
[7].
R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, and S. McBride, "A Machine-Vision
System for Iris Recognition," Machine Vision and Applications, vol. 9, pp. 1-8, 1996
[8].
Makram Nabti and Bouridane, "An effective iris recognition system based on wavelet maxima and Gabor
filter bank", IEEE trans. on iris recognition, 2007.
[9].
Narote et al. "An iris recognition based on dual tree complex wavelet transform". IEEE trans. on iris
recognition, 2007.
[10]. Institute of Automation Chinese Academy of Sciences. Database of CASIA iris image [EB/OL]
[11]. L. Masek, "Recognition of Human Iris Patterns for Biometric Identification", The University of
Western California, 2003.
[12]. N. G. Kingsbury, "Image processing with complex wavelets," Philos.Trans. R. Soc. London A,
Math. Phys. Sci, vol. 357, no. 3, pp. 2543-2560, 1999.
[13]. Vijay M.Mane, GauravV. Chalkikar and Milind E. Rane, "Multiscale Iris Recognition System",
International journal of Electronics and Communication Engineering & Technology (IJECET),
Volume 3, Issue 1, 2012, pp. 317 - 324, ISSN Print: 0976- 6464, ISSN Online: 0976 -6472.
[14]. Darshana Mistry and Asim Banerjee, "Discrete Wavelet Transform using Matlab", International
journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2012, pp. 252 - 259,
ISSN Print: 0976 - 6367, ISSN Online: 0976 - 6375.
[15]. Sayeesh and Dr. Nagaratna p. Hegde “A comparison of multiple wavelet algorithms for iris
Recognition” International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online) Volume 4, Issue 2, March - April (2013), © IAE
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HIDING THE DATA USING STEGANOGRAPHY
WITH DIGITAL WATERMARKING
1
G.Thirumani Aatthi, 2A.Komathi
1
Research Scholar, Department of Computer Science & Information Technology,
Nadar Saraswathi College of Arts & Science, Theni,Tamil Nadu,(India)
2
Department of Computer Science & Information Technology;
Nadar Saraswathi College of Arts & Science, Theni, Tamil Nadu,(India)
ABSTRACT
Data Security is the method of shielding Information. It protects its accessibility, privacy and Integrity. Access
to Stored data on computer data base has improved greatly. More Companies store business and individual
information on computer than ever before. Much of the data stored is highly confidential and not for public
viewing. Cryptography and steganography are well known and widely used techniques that manipulate
information in order to cipher or hide their existence. These two techniques share the common goals and
services of protecting the confidentiality, integrity and availability of information from unauthorized access. In
Existing research, data hiding system that is based on image steganography and cryptography is proposed to
secure data transfer between the source and destination. In this the main drawback was that, the hackers may
also get the opportunity to send some information to destination and it may lead confusion to receiver.
In my research I proposed LSB(Leased Significant Bit) Technique used for finding the image pixel position and
pseudorandom permutation method used for store the data in random order. Moreover I have proposed digital
watermark technique to avoid the unauthorized receiving information from hackers. In this proposed system, the
above three technique will be combined for secure data transfer. Experimental results will prove the efficiently
and security of my Proposed work.
Key Word: Cryptography, Steganography, Data Security, Key Generation
I. INTRODUCTION
In the Internet one of the most important factors of information technology and communication has been the
security of information. Cryptography was created as a technique for securing the secrecy of communication
and many different methods have been developed to encrypt and decrypt data in order to keep the message
secret. Unfortunately it is sometimes not enough to keep the contents of a message secret, it may also be
necessary to keep the existence of the message secret. The technique used to implement this, is called
steganography.
Steganography is the art and science of invisible communication. This is accomplished through hiding
information in other information, thus hiding the existence of the communicated information. All digital file
formats can be used for steganography, the four main categories of file formats that can be used for
steganography.
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Figure 1: Categories of Steganography
Over the past few years, numerous steganography techniques that embed hidden messages in multimedia objects
have been proposed. There have been many techniques for hiding information or messages in images in such a
manner that the alterations made to the image are perceptually indiscernible. Common approaches are include:
(i) Least significant bit insertion (LSB)
(ii) Masking and filtering
(iii) Transform techniques
Least significant bits (LSB) insertion is a simple approach to embedding information in image file. The simplest
steganographic techniques embed the bits of the message directly into least significant bit plane of the coverimage in a deterministic sequence. Modulating the least significant bit does not result in human-perceptible
difference because the amplitude of the change is small.
Masking and filtering techniques performs analysis of the image, thus embed the information in significant areas
so that the hidden message is more integral to the cover image than just hiding it in the noise level.
Transform techniques embed the message by modulating coefficients in a transform domain, such as the
Discrete Cosine Transform (DCT) used in JPEG compression, Discrete Fourier Transform, or Wavelet
Transform. These methods hide messages in significant areas of the cover-image, which make them more robust
to attack. Transformations can be applied over the entire image, to block through out the image, or other
variants.
II. LITERATURE REVIEW
Here, Ms.Dipti and Ms.Neha, developed a technique named, “Hiding Using Cryptography and
Steganography”[01]is discussed. In that, Steganography and Cryptography are two popular ways of sending vital
information in a secret way. One hides the existence of the message and the other distorts the message itself.
There are many cryptography techniques available; among them AES is one of the most powerful techniques. In
Steganography we have various techniques in different domains like spatial domain, frequency domain etc. to
hide the message. It is very difficult to detect hidden message in frequency domain and for this domain we use
various transformations like DCT, FFT and Wavelets etc. They are developing a system where we develop a
new technique in which Cryptography and Steganography are used as integrated part along with newly
developed enhanced security module. In Cryptography we are using AES algorithm to encrypt a message and a
part of the message is hidden in DCT of an image; remaining part of the message is used to generate two secret
keys which make this system highly secured.
Another newly developed Method named,“Two New Approaches for Secured Image Steganography Using
Cryptographic Techniques and Type Conversions. Signal & Image Processing”
[02]
is discussed. Mr. Sujay, N.
and Gaurav, P,introduces two new methods wherein cryptography and steganography are combined to encrypt
the data as well as to hide the encrypted data in another medium so the fact that a message being sent is
concealed. One of the methods shows how to secure the image by converting it into cipher text by S-DES
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algorithm using a secret key and conceal this text in another image by steganographic method. Another method
shows a new way of hiding an image in another image by encrypting the image directly by S-DES algorithm
using a key image and the data obtained is concealed in another image.
Another newly developed Method named, “ Image Based Steganography and Cryptography “[06] is discussed. In
that Mr. Domenico, B. and Luca, L. year.. describe a method for integrating together cryptography and
steganography through image processing. In particular, they present a system able to perform steganography and
cryptography at the same time using images as cover objects for steganography and as keys for cryptography.
They will show such system is an effective steganographic one (making a comparison with the well known F5
algorithm) and is also a theoretically unbreakable cryptographic one (demonstrating its equivalence to the
Vernam Cipher)
III. PROPOSED WORK
In this paper I proposed, Data hiding in media, including images, video, and audio, as well as in data files is
currently of great interest both commercially, mainly for the protection of copyrighted digital media, and to the
government and law enforcement in the context of information systems security and covert communications. So
I present a technique for inserting and recovering “hidden” data in image files as well as gif files. Each Color
pixel is a combination of RGB Values wherein each RGB components consists of 8 bits. If the letters in ASCII
are to be represented within the color pixels, the rightmost digit, called the Least Significant Bit (LSB) can be
altered to hide the images.
3.1 Key Stream Generation
In order to encrypt the message, we choose a randomly generated key-stream. Then the encryption is done byte
by byte to get the ciphered text.
The key stream is generated at the encryption and decryption site.
For encryption, a secret seed is applied to the content which in turn generates the key stream. In order to
generate the same key at the decryption site, the seed must be delivered to the decryption site through a secret
channel.Once the seed is received, it can be applied to the cipher text to generate the key stream which is further
used for decryption
C = E(M,K) = (Mi+Ki) mod 255
where i=0 to L-1
Where M is Message
K is randomly generated key-stream.
3.2 Watermarking Algorithm
3.2.1 SS (Spread Spectrum)
We proposed a spread spectrum watermarking scheme. The embedding process is carried out by first generating
the watermark signal by using watermark information bits, chip rate and PN sequence.The watermark
information bits b = {bi}, where bi = {l, -1}, are spread by, which gives aj=bi
The watermark signal W = {wj}, where
wj = ajPj
where Pj= {l, -1}
The watermark signal generated is added to the encrypted signal, to give the watermarked signal
CW= C + W = Cwi= ci + wi.
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IV. SAMPLE SCREEN SHOTS
Send the Message
Receive the Message
Encrypt & Validation Code
Verification Encryption Key & Validation Code
Decrypt: (Water Marking With Text)
V. CONCLUSION
I propose a novel technique to embed a robust watermark in the JPEG2000 compressed encrypted images using
three different existing watermarking schemes. The algorithm is simple to implement as it is directly performed
in the compressed-encrypted domain, i.e., it does not require decrypting or partial decompression of the content.
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The scheme also preserves the confidentiality of content as the embedding is done on encrypted data. The
homomorphic property of the cryptosystem are exploited, which allows us to detect the watermark after
decryption and control the image quality as well. The detection is carried out in compressed or decompressed
domain. In case of decompressed domain, the non-blind detection is used. I analyze the relation between
payload capacity and quality of the image (in terms of PSNR and SSIM) for different resolutions. Experimental
results show that the higher resolutions carry higher payload capacity without affecting the quality much,
whereas the middle resolutions carry lesser capacity and the degradation in quality is more than caused by
watermarking higher resolutions.
REFERENCES
[1]. Dipti, K. S. and Neha, B. 2010. Proposed System for Data Hiding Using Cryptography and Steganography.
International Journal of Computer Applications. 8(9), pp. 7-10. Retrieved 14th August, 2012
[2]. Sujay, N. and Gaurav, P. 2010. Two New Approaches for Secured Image Steganography Using
Cryptographic Techniques and Type Conversions. Signal & Image Processing: An International Journal
(SIPIJ), 1(2), pp 60-73.
[3]. Domenico, B. and Luca, L. year. Image Based Steganography and Cryptography.
[4]. Jonathan Cummins, Patrick Diskin, Samuel and Robert Par-lett,“Steganography and Digital
Watermarking”, 2004.
[5]. Clara Cruz Ramos, Rogelio Reyes Reyes, Mariko Nakano Miyata-keandHéctor Manuel Pérez Meana,
“Watermarking-Based Image Authentication System in the Discrete Wavelet Transform Domain”.intechopen.
[6]. Domenico, B. and Luca, L. year. Image Based Steganography and Cryptography.
[7]. Niels, P. and Peter, H 2003. Hide and Seek: An Introduction to Steganography. IEEE Computer Society.
IEEE Security and Privacy, pp. 32-44.
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IMPROVING DATA SECURITY IN AD-HOC
NETWORK BASED ON CPHS ALGORITHM
N.Vandhana1, P.Nithya2
1
Research Scholar, Department of Computer Science & Information Technology
Nadar Saraswathi College of Arts & Science, Theni,Tamil Nadu, (India)
2
Department of Computer Science & Information Technology
Nadar Saraswathi College of Arts & Science, Theni,Tamil Nadu, (India)
ABSTRACT
An ad-hoc network is a temporary network connection created for a specific purpose such as transferring data
from one computer to another. Ad-hoc networks confirmed their efficiency being used in different fields but they
are highly vulnerable to security attacks and dealing with this is one of the main challenges of these networks
today. In Existing System, the data transmission is based only on the encryption technique and follows the same
arrangement of the data frame. In this the main drawback was that the hackers may also know the arrangement
of the data frame. So the data will be easily hacked or damaged by the hacker.
In this research, the proposed CPHS (Crypto Puzzle Hiding System) algorithm is based on key generation
algorithm, used for encrypt the data and change the arrangement of the data frame. And the hackers will be
confused to decrypt the data. So the research will provide the security for data transmission. Experiments
results will prove the efficient and security of my proposed work.
I. INTRODUCTION
The open nature of the wireless medium leaves it vulnerable to intentional interference attacks, typically
referred to as jamming. This intentional interference with wireless transmissions can be used as a launch pad for
mounting Denial-of-Service attacks on wireless networks. Typically, jamming has been addressed under an
external threat model. However, adversaries with internal knowledge of protocol specifications and network
secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work, we address
the problem of selective jamming attacks in wireless networks.
In these attacks, the adversary is active only for a short period of time, selectively targeting messages of high
importance. We illustrate the advantages of selective jamming in terms of network performance degradation and
adversary effort by presenting two case studies; a selective attack on TCP and one on routing. We show those
selective jamming attacks can be launched by performing real-time packet classification at the physical layer.
To mitigate these attacks, we develop three schemes that prevent real-time packet classification by combining
cryptographic primitives with physical layer attributes. We analyze the security of our methods and evaluate
their computational and communication overhead.
Wireless networks rely on the uninterrupted availability of the wireless medium to interconnect participating
nodes. However, the open nature of this medium leaves it vulnerable to multiple security threats. Anyone with a
transceiver can eavesdrop on wireless transmissions, inject spurious messages, or jam legitimate ones. While
eavesdropping and message injection can be prevented using cryptographic methods, jamming attacks are much
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harder to counter. They have been shown to actualize severe Denial-of-Service (DoS) attacks against wireless
networks. In the simplest form of jamming, the adversary interferes with the reception of messages by
transmitting a continuous jamming signal, or several short jamming pulses. Typically, jamming attacks have
been considered under an external threat model, in which the jammer is not part of the network. Under this
model, jamming strategies include the continuous or random transmission of high-power interference signals.
However, adopting an “always-on” strategy has several disadvantages.
First, the adversary has to expend a significant amount of energy to jam frequency bands of interest. Second, the
continuous presence of unusually high interference levels makes this type of attacks easy to detect.
Conventional anti-jamming techniques rely extensively on spread-spectrum (SS) communications, or some form
of jamming evasion (e.g., slow frequency hopping, or spatial retreats). SS techniques provide bit-level
protection by spreading bits according to a secret pseudo-noise (PN) code, known only to the communicating
parties.
These methods can only protect wireless transmissions under the external threat model. Potential disclosure of
secrets due to node compromise neutralizes the gains of SS. Broadcast communications are particularly
vulnerable under an internal threat model because all intended receivers must be aware of the secrets used to
protect transmissions. Hence, the compromise of a single receiver is sufficient to reveal relevant cryptographic
information. In this paper, we address the problem of jamming under an internal threat model.
II. RESULT AND DISCUSSION
2.1. Hiding Based On Cryptographic Puzzles
We present a packet hiding scheme based oncryptographic puzzles. The main idea behind such puzzlesis to
force the recipient of a puzzle execute a pre-definedset of computations before he is able to extract a secret
ofinterest. The time required for obtaining the solution ofa puzzle depends on its hardness and the computational
ability of the solver. The advantage of the puzzlebased scheme is that its security does not rely on the PHYlayer
parameters. However, it has higher computation andcommunication overhead.
Fig 2: The Cryptographic Puzzle Based Hiding Scheme
In our context, we use cryptographic puzzles to temporary hide transmitted packets. A packet m is
encryptedwith a randomly selected symmetric key k of a desirablelength s. The key k is blinded using a
cryptographic puzzleand sent to the receiver. For a computationally boundedadversary, the puzzle carrying k
cannot be solved beforethe transmission of the encrypted version of m is completedand the puzzle is received.
Hence, the adversary cannotclassify m for the purpose of selective jamming.
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2.1.1. Cryptographic Puzzle Hiding Scheme (CPHS)
Let a sender S have a packet m for transmission. The sender selects a random key k ∈ {0, 1} s , of a desired
length. S generates a puzzle P = puzzle(k, tp), where puzzle() denotes the puzzle generator function, and tp
denotes the time required for the solution of the puzzle. Parameter tp is measured in units of time, and it is
directly dependent on the assumed computational capability of the adversary, denoted by N and measured in
computational operations per second. After generating the puzzle P , the sender broadcasts (C, P ), where C =
Ek (π 1(m)). At the receiver side, any receiver R solves the received puzzle P to recover key k ′ and then
computes m′= π −1 (Dk′ (C′)). If the decrypted packet m′ is meaningful (i.e., is in the proper format, has a valid
CRC code, and is within the context of the receiver’s communication), the receiver accepts that m′ = m. Else,
the receiver discards m′. Fig. 2 shows the details of CPHS.
III. CONCLUSION AND FUTURE ENHANCEMENT CONCLUSION
We addressed the problem of selective jamming attacks in wireless networks. We considered an internal
adversary model in which the jammer is part of the network under attack, thus being aware of the protocol
specifications and shared network secrets. We showed that the jammer can classify transmitted packets in real
time by decoding the first few symbols of an ongoing transmission. We evaluated the impact of selective
jamming attacks on network protocols such as TCP and routing. Our findings show that a selective jammer can
significantly impact performance with very low effort. We developed three schemes that transform a selective
jammer to a random one by preventing real-time packet classification. Our schemes combine cryptographic
primitives such as commitment schemes, cryptographic puzzles, and all-or-nothing transformations (AONTs)
with physical layer characteristics. We analyzed the security of our schemes and quantified their computational
and communication overhead.
BIBLIOGRAPHY
[1]. T. X. Brown, J. E. James, and A. Sethi. Jamming and sensing of encrypted wireless ad hoc networks. In
Proceedings of MobiHoc, pages 120–130, 2006.
[2]. M. Cagalj, S. Capkun, and J.-P. Hubaux. Wormhole-based antijamming techniques in sensor networks.
IEEE Transactions on MobileComputing, 6(1):100–114, 2007.
[3]. A. Chan, X. Liu, G. Noubir, and B. Thapa. Control channel jamming: Resilience and identification of
traitors. In Proceedings of ISIT, 2007.
[4]. T. Dempsey, G. Sahin, Y. Morton, and C. Hopper. Intelligent sensing and classification in ad hoc
networks: a case study. Aerospace andElectronic Systems Magazine, IEEE, 24(8):23–30, August 2009.
[5]. Y. Desmedt. Broadcast anti-jamming systems. Computer Networks, 35(2-3):223–236, February 2001.
[6]. K. Gaj and P. Chodowiec. FPGA and ASIC implementations of AES. Cryptographic Engineering, pages
235–294, 2009.
[7]. O. Goldreich. Foundations of cryptography: Basic applications. Cambridge University Press, 2004.
[8]. B. Greenstein, D. Mccoy, J. Pang, T. Kohno, S. Seshan, and D. Wetherall. Improving wireless privacy
with an identifier-free link layer protocol. In Proceedings of MobiSys, 2008.
[9]. A. Juels and J. Brainard. Client puzzles: A cryptographic countermeasure against connection depletion
attacks. In Proceedings of NDSS, pages 151–165, 1999.
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[10]. Y. W. Law, M. Palaniswami, L. V. Hoesel, J. Doumen, P. Hartel, and P. Havinga. Energy-efficient linklayer jamming attacks against WSN AC protocols. ACM Transactions on Sensors Networks, 5(1):1–38,
2009.
WEB SITES
1.
http://www.sourcefordgde.com
2.
http://www.networkcomputing.com/
3.
http://www.almaden.ibm.com/software/quest/Resources/
4.
http://oceanstore.cs.berkely.edu
5.
http://ww.amazon.com/gp/browse amazing store
6.
http://en.amazingstore.org/,2013
7.
clever safe inc., http://www.cleversafe.org/dispersed-storage,2013
8.
http://www.overnet.com
9.
http://www.netcraft.com
10. http://ramp.uecd.edu/projects/recall
11. http://www.seagate.com/support/kd/disc/smart.html,2013
12. http://simpy.sourceforge.ref/(2013)
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DESIGN AND IMPLEMENTATION OF ADAPTIVE
ECHO CANCELLER BASED LMS & NLMS
ALGORITHM
Sandip A. Zade1, Prof. Sameena Zafar2
1
Mtech student,Department of EC Engg., Patel college of Science and Technology Bhopal(India)
2
Acadamic Dean. Patel College of Science and Technology Bhopal(India)
ABSTRACT
This paper proposes the adaptive echo cancellation using normalized least mean square (NLMS) algorithm. The
NLMS algorithm is the normalized version of least mean square (LMS) algorithm. Todays technical scenario
communication system, possesses additive noise, signal interference and echo etc. Due to this reason the error
is generated at the time of transmission of data. Hence adaptive filter is a appropriate option to reduce the
noise or channel effects. In this paper high speed of adder, substractor and Adaptive filter coefficients to design
LMS and NLMS algorithm is realized. This paper focuses on the use of LMS and NLMS algorithms to reduce
this unwanted echo, thus increasing communication quality. This work better highlights differences between
algorithm performance than previously published work and sheds new light on algorithm behavior in terms of
SNR. This work were created in the popular VHDL language. The NLMS algorithm establishes a better balance
between simplicity and performance than least mean square algorithm.
Keywords: Adaptive Filter, NLMS Algorithm, LMS Algorithm, Vhdl Language
I. INTRODUCTION
An echo is a reflection of sound, arriving at the listener some time after the direct sound. Echo is the reflected
copy of the voice heard some time later and delayed version of the original. The term echo cancellation is used
in telephony to describe the process of removing echo from a voice communication in order to improve voice
quality on a telephone call. Echo cancellation is the process which removes unwanted echoes from the signal on
a telephone line. It includes first recognizing the originally transmitted signal that re-appears, with some delay, in
the transmitted or received signal. Once the echo is recognized, it can be removed by 'subtracting' it from the
transmitted or received signal. Multiple reflections in acoustic enclosures and transmission delay affect the sound
quality, which in the case of a teleconferencing system lead to a poor understanding of the conversation. In
addition to improving subjective quality, this process increases the capacity achieved through silence
suppression by preventing echo from traveling across a network. Hands-free phone is a basic and intrinsic
application with small information terminals such as cell phones, smart phones, and tablet PCs. For hands-free
communication, echo cancellation is common but still a difficult function. An echo canceller has an adaptive
filter to emulate the echo path between the input of amplifier to drive a loudspeaker and the microphone. Even
with echo cancellers, suppression of echo is very difficult because the loudspeaker is small and close to the
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microphone, and the sound from the loudspeaker is very loud. Echo cancellation try to eliminate the echo from
the transmitted audio signal with a special optimization algorithm. The algorithm produces copies of the received
signal and checks for parts of the signal that reappear with some delay. This reappearing parts are then
subtracted from the signal, the echo is removed. In this paper, the LMS and NLMS adaptive algorithms are
described. The following sub-sections will present a brief description of
adaptive filters focused on the
cancellation of noise and echo. The second section if focused on briefly review of the adaptive filtering
algorithm. The third section presents acoustic echo cancellation. The fourth section presents literature survey.
The fifth section shows the methodology used. The last section presents the simulation and experimental results
obtained from the adaptive cancellation of noise & it summarizes the main findings of the paper.
II. VARIOUS ADAPTIVE FILTERING ALGORITHMS
A filter which adapts itself to the input signal given to it. Adaptive Filtering dealing with adaptive filters and
system design. The main function of adaptive filtering is the development of a filter capable of adjusting to the
statistics of the signal. Usually, an adaptive filter takes the form of a FIR filter, with an adaptive algorithm that
modifies the values of its coefficients. They are used in a wide range of applications including system
identification, noise cancellation, signal prediction, echo cancellation and adaptive channel equalization,
process control. The main configurations of adaptive filters are the adaptive cancellation of noise and echo. For
this justification, the filter uses an adaptive algorithm to change the value of the filter coefficients, so that it
acquires a better approximation of the signal after each iteration. The LMS, and its variant the NLMS are two of
the adaptive algorithms widely in use. Also the normalized version of the LMS algorithm, called Normalized
Least Mean Square (NLMS) algorithm is widely used. NLMS algorithm has been used more often in real time
applications. The LMS algorithm have slow convergence and poor tracking as compare to the the NLMS
algorithm. Both algorithms require a small number of multiplications and additions for the update of the
coefficients.
2.1 LMS Algorithm [9]
The LMS algorithm adapts the filter tap weights so that e(n) is minimized in the mean-square sense. LMS
algorithm is sensitive to variation in step size parameter. LMS algorithm requires number of itrations equal to
dimensionality of the input. When the processes x(n) & d(n) are jointly stationary, this algorithm converges to a
set of tap-weights which, on average, are equal to the Wiener-Hoff solution. It acts as a negative feedback to
minimize error signal.
1. The output of the FIR filter, y(n) is
2. The value of the error estimation is
e(n) = d(n) - y(n)
3. The tap weights of the FIR vector are updated in preparation for the next iteration
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The main reason for the LMS algorithms popularity in adaptive filtering is its computational simplicity, making
it easier to implement than all other commonly used adaptive algorithms. The only disadvantage is its weak
convergence. For each iteration the LMS algorithm requires 2N additions and 2N+1 multiplication. Where n=
0,1,2,....n.
1.2 NLMS Algorithm [9]
As the NLMS is an extension of the standard LMS algorithm, the NLMS algorithms practical implementation is
very similar to that of the LMS algorithm. One of the drawback of LMS algorithm is selection of step size
parameter. In order to solve this difficulty we can use the NLMS algorithm. Here the step size parameter is
normalized.So the NLMS algorithm is time varying step size algorithm. The NLMS algorithm is used in system
in order to improve voice quality. Each iteration of the NLMS algorithm requires these steps in the following
order
1. The output of the adaptive filter is calculated
2. An error signal is calculated as the difference between the desired signal and the filter output
e(n) = d(n) – y(n)
3. The step size value for the input vector is calculated
4. The filter tap weights are updated in preparation for the next iteration
w(n+1) = w(n) + µ(n)e(n)x(n)
Each iteration of the NLMS algorithm requires 3N+1 multiplications, this is only N more than the standard
LMS algorithm. This is an acceptable increase considering the gains in stability and echo attenuation
achieve[10]. Where n= 0,1,2,....n.
Adaptive filters are systems with four terminals as shown in Fig.1, where x is the input signal, d is the desired
signal, y is output signal of filter and e is the error signal [4]. Adaptive filters design technique may be digital,
analog or mixed. Every technique presents advantages and disadvantages, for example, analog adaptive filters
are very fast, but offset avoids getting the least error [4]. Digital filters are slow but precise, because is necessary
the use of a lot of components, due to floating point operations. Mixed design (analog and digital), offers a good
compromise between precision and speed, but VLSI design is more complicated because is necessary to separate
analog and digital components inside the chip .The LMS algorithm is one of the most used algorithms because it
is easy and stable. The only disadvantage is its weak convergence [3]. The length of the FIR filter is 64 and the
size of the step is 0.001.
Two inputs are required:
· A reference noise that should be related with the noise that exists in distorted the input signal. This means
that the noise comes from the same source.
· An error signal already calculated.
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Figure 1 Adaptive Filter
III. ACOUSTIC ECHO CANCELLATION (AEC)
The AECs is a system identification application as shown in Fig.2, which uses adaptive filters to obtain a copy of
the acoustic transfer function (the response of an enclosure to an acoustic impulse). Acoustic echo occur when an
audio signal is reverberated in a real environment, resulting in the original intended signal plus attenuated and
delay time. The signal applied to the loudspeaker(s) x(n) propagates through multiple acoustic paths and it is
picked up by the microphone(s). This signal is used as the desired signal d(n) in the system identification
process. The output of the adaptive filter y(n) is determined by convolving the samples x(n) with the adaptive
filter coefficients w(n).The filter is altered iteratively to minimize the error signale(n). The coefficient update can
be carried out with various adaptive algorithms.
Figure 2 Acoustic Echo Cancellation
Public addressing systems are affected by acoustic feedback that may lead to the saturation of the system. In
order to improve the sound quality and prevent audio feedback the acoustic echo cancellers (AECs) are deployed
to remove the undesired echoes resulting from the acoustic coupling between the loudspeaker(s) and the
microphone(s).
IV. LITERATURE SURVEY
The paper [2] presents a solution to noise or echo cancellation and a hardware real-time implementation of the
LMS algorithm. The overall performance of the designed adaptive filter compared with other implemented
systems using the same filter is good, and with further improvements the results will improve. The LMS
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algorithm provides good numerical stability and its hardware requirements are low. On the other hand, the
NLMS algorithm is one of the most implemented adaptive algorithms in actual industrial applications.
This paper [3] proposes an FPGA implementation of an Adaptive Noise Canceller using the LMS algorithm. In
this paper, in order to show the performance of FPGA in digital signal processing applications, implement an
Adaptive Noise Canceller on an FPGA and use the LMS algorithm as the adaptive filtering algorithm of the
Adaptive Noise Canceller. The performance of the LMS algorithm implemented by hardware is comprehensively
analyzed in terms of convergence performance, truncation effect and tracking ability.
In paper [4] an echo canceller is presented, using an adaptive filter with a modified LMS algorithm, where this
modification is achieved coding error on conventional LMS algorithm.
V. METHODOLOGY USED
The architecture of adaptive filter is already implemented with LMS Algorithm for Adaptive echo cancellation.
Also the comparison between LMS & NLMS algorithm is being done. The NLMS algorithm outperforms the
LMS algorithm, in terms of Mean Square Error. It has a better convergence than LMS algorithm. The NLMS
adaptive filtering algorithm is expressed by its simplicity in implementation and its stability. These advantages
recommend the NLMS algorithm as a good choice for real time implementation. Hence the NLMS algorithm can
be implemented for Adaptive echo cancellation in VHDL. Also the comparison of LMS and NLMS algorithm
for echo cancellation along with signal to noise ratio has been done. Fig.3 shows the architecture used. The
system architecture consist of main four modules such as 16 bit adder, 16 bit substractor, multiplier and delay.
Figure 3 System Architecture
Where, x(n),x(n-1),……,x(n-M+1) is the tap inputs
x(n) is the reference signal
M-1 is the number of delay elements.
d(n) denotes the primary input signal
e(n) denotes the error signal and
wi(n) denotes the tap weight at the nth iteration..
VI. RESULT AND CONCLUSIONS
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6.1 VHDL Result for Signal 1
Figure 4 Training Signal 1 of LMS algorithm
Figure 5 Testing signal 1 of LMS algorithm
Figure 6 Training signal 1 of NLMS algorithm
Figure 7 Testing signal 1 of NLMS algorithm
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6.2 MATLAB Results for LMS & NLMS Algorithm
Figure 8 Signal 1 of LMS Algorithm
Figure 9 Signal 1 of NLMS Algorithm
The PSNR of the LMS algorithm is 4.08 dB and the PSNR of the NLMS algorithm is 29.72 dB. Whereas the
mean square error of the LMS algorithm is 0.39 and the mean square error of the NLMS algorithm is 0.00106.
Figure 10 Signal 2 of LMS algorithm
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Figure 11 Signal 2 of NLMS Algorithm
The PSNR of the LMS algorithm is 2.88 dB and the PSNR of the NLMS algorithm is 22.17 dB. Whereas the
mean square error of the LMS algorithm is 0.514 and the mean square error of the NLMS algorithm is 0.006.
The adaptive echo canceller based LMS and NLMS algorithm is successfully designed wherein comparison of
LMS and NLMS algorithm has been done in terms of PSNR. The PSNR of the NLMS algorithm is 25.945 dB
and the PSNR of the LMS algorithm is 3.48dB. So the PSNR is maximum it realize better sound quality.
REFERENCES
[1]
I. Homana, et al, “FPGA Implementation of LMS and NLMS Adaptive Filter for Acoustic Echo
Cancellation”, in Journal of Electronics & Telecommunication, vol 52, no. 4, 2011.
[2]
C. G. Saracin, et al, “Echo Cancellation using LMS Algorithm”, in UPB Science Bulletin Series C,vol 71,
no 4, 2009.
[3] TianLan, et al, “FPGA Implementation of Adaptive Noise Canceller”, in IEEE International Symposium
on Information Processing, 2008.
[4]
J. V. Lopez, et al, “Adaptive Echo Canceller using Modified LMS Algorithm”, in IEEE International
Conference on Electrical & Electronics Engineering, 2005.
[5]
R. Dony, et. al., “An FPGA Implementation of the LMS Adaptive Filter for Audio Processing”, Proc. of
the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM04), pp.
324- 335, 2006. [6]
Elhossini, S. Areibi and R. Dony, “An FPGA Implementation of the LMS Adaptive
Filter for Audio Processing,” in Proc. IEEE International Conference on Reconfigurable Computing and
FPGAs, Sept. 2006, pp
[7]
L. K. Ting, R. F. Woods and C. F. N. Cowan, “Virtex FPGA Implementation of a Pipelined Adaptive LMS
Predictor for Electronic Support Measures Receivers,” IEEE Trans. VLSI Syst., vol. 13, Jan. 2005, pp. 8695.
[8]
P. Waldeck and N. Bergmann, “Evaluating software and hardware implementations of signal-processing
tasks in an
FPGA,” in Proc. IEEE International Conference on Field-Programmable Technology,
Brisbane, Australia, Dec.
2004, pp. 299-302.
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[9]
ISBN: 978-81-931039-3-7
Alok Pandey, L.D. Malviya , Vineet Sharma “ Comparative Study of LMS and NLMS Algorithms in
Adaptive Equalizer”, International Journal of Engineering Research and Applications (IJERA), Vol. 2,
Issue 3, May-Jun 2012, pp.1584-1587.
[10] Radhika Chinaboina, 1D.S.Ramkiran, 2Habibulla Khan, 1M.Usha, 1B.T.P.Madhav, 1K.Phani Srinivas &
1G.V.Ganesh,“Adaptive
Algorithms For Acoustic Echo Cancellation In Speech Processing”,
www.arpapress.com/Volumes/Vol7Issue1/IJRRAS, April 2011
[11] S. Haykin, Adaptive Filter Theory, Fourth Edition, Prentice Hall, Upper Saddle River, N.J., 2002.
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EFFICIENT DETECTION IN DDOS ATTACK FOR
TOPOLOGY GRAPH DEPENDENT PERFORMANCE
IN PPM LARGE SCALE IPTRACEBACK
S.Abarna1, R.Padmapriya2
1
Mphil Scholar, 2Assistant Professor, Department of Computer Science,
NadarSaraswathi College (India)
ABSTRACT
A variety of schemes based on the technique of Probabilistic Packet Marking (PPM) have been proposed to
identify Distributed Denial of Service (DDoS) attack traffic sources by IP trace back. These PPM-based
schemes provide a way to reconstruct the attack graph - the network path taken by the attack traffic - hence
identifying its sources. Despite the large amount of research in this area, the influence of the underlying
topology on the performance of PPM-based schemes remains an open issue. Distributed Denial-of-Service
(DDoS) attacks are a critical threat to the Internet. However, the memory less feature of the Internet routing
mechanisms makes it extremely hard to trace back to the source of these attacks. In this paper, we propose a
novel trace back method for DDoS attacks that is based on entropy variations between normal and DDoS attack
traffic, which is fundamentally different from commonly used packet marking techniques.we identify five
network-dependent factors that affect different PPM-based schemes uniquely giving rise to a variation in and
discrepancy between scheme performances from one network to another. Using simulation, we also show the
collective effect of these factors on the performance of selected schemes in an extensive set of 60 Internet-like
networks. We find that scheme performance is dependent on the network on which it is implemented. We show
how each of these factors contributes to a discrepancy in scheme performance in large scale networks. This
discrepancy is exhibited independent of similarities or differences in the underlying models of the networks.
I. INTRODUCTION
Internet Protocol (IP) trace back is a technique for identi- fying the sources of Distributed Denial of Service
(DDoS) attacks from its traffic . One approach to implementing IP trace back ensures that the routers embed
their identity in packets randomly selected from all the packets they process . In the event of an attack, the
victim uses the packets that contain router identities to construct an attack graph. The attack graph is a
representation of the routers and links that the attack packets traversed from the attacker(s) to the victim. This IP
trace back type is called probabilistic packet marking (PPM) and is implemented by PPM-based schemes. A lot
of intensive research has gone into designing PPM- based schemes that are computationally more efficient and
robust than the original PPM. However, little work has gone into identifying network-dependent factors that
affect the performance of PPM-based schemes in large-scale networks. In fact, most simulations are carried out
on disparate tree- structured topologies and the analytical models derived from these topologies are used to
predict the performance of the schemes when deployed in a large-scale network such as the Internet . However,
since the schemes are implemented on disparate networks, it is difficult to directly compare the performance of
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different schemes. Furthermore, because typical underlying topologies are tree-structured, it is difficult to make
appropriate projections about scheme performance in a large-scale network without implementing the scheme
on that network. In this work, we show the influence of network topology on PPM-based scheme performance.
We identify three network- dependent factors that affect scheme performance in large- scale networks. These
factors include average shortest path length, overlapping of attack paths, and the occurrence of network motifs
in attack graphs. Using specific attack graphs, we show the influence of each factor on selected PPM- based
schemes. We then use 60 Internet-like networks to show how all the identified factors collectively contribute to
the performance of PPM-based schemes in more realistic scenarios. The networks are selected to encompass the
variety of mathematical models used by researchers to create networks that adequately describe the structure of
the Internet. Results show that PPM-based scheme performance is de- pendent on the network on which it is
implemented. In fact, even the order of performance changes from one network to another, i.e. the best
performing scheme in one network is not necessarily the best performing scheme in another network. Our
results show how the identified factors contribute, both individually and collectively, to the PPM-based
schemes’ performance in large scale networks.
II.UNDERLYING TOPOLOGIES
A variety of underlying topologies have been used to evaluate the performance of PPM-based schemes. Some
schemes use a single path single attacker (SP/SA) topology to simulate a Denial of Service (DoS) attack [2], [3],
[5], [6], [8]. In these cases, the length of the attack path is different ranging from 3 hops to 32 hops. Other
schemes utilize trees as their underlying topologies for simulation. These include binary trees in [4], [3], [10]
ranging from 6 hops to 10 hops. Other tree structures such as random tree networks are used in [3], [8]. Yet
other schemes utilize internet topology datasets such as traceroute datasets and skitter maps [9]. While these
topologies are significantly larger than other topologies (up to 174,409 nodes), they do not easily lend
themselves to simulations and, consequently, comparisons with other schemes.
Fig 2. Binary Tree Topology
III. SIMULATION STUDY
The 60 networks that are considered in this study and some of their properties. These properties include the
setup properties such as the underlying model, and appropriate settings required to build each specific network.
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Additionally, network specific properties, e.g. average shortest path length, and network motif IDs are shown.
Each of the networks consists of 1000 nodes representing routers in a network, all of which employ the marking
schemes. One of the nodes is selected to be the victim and 50 other nodes are randomly selected to be the
attackers. Constant Bit Rate (CBR) sources of traffic are implemented at the attackers, and the convergence time
for the entire attack graph is measured in packets. This simulation is executed 200 times for each network and
each marking scheme.
Fig 3. (a) Number of Network Attack Notes
Fig 3. (b) Constant Bit Rate
IV. OVERLAPPING OF ATTACK PATHS
In this subsection, we show how the level of overlap between two attack paths affects the schemes’ convergence
times. We consider a Y-shaped attack graph linking attackers A1 and A2 to victim V (cf. Fig. 2). While keeping
each attack path equal and constant, we vary the amount of overlap between the attack paths and observe how
the convergence times of PPM, TMS, and PBS are affected. Fig. 6 shows the observed results from this
investigation. The results show that there is a general reduction in con- vergence times for all considered
schemes as the percentage overlap is increased. Despite the general reduction for all 3 considered schemes, the
level of overlap affects each scheme uniquely. For example, the results show that PPM and TMS are relatively
unaffected by low amounts of overlap, i.e. 0-20% while PBS exhibits a reduction in convergence times in the
same overlap range. However, further increase in percentage overlap causes a drastic decrease in the
convergence time of TMS such that by 60%-70% TMS has lower conver- gence times than both PBS and PPM.
These results show three things: Firstly, larger amounts of overlapping attack paths translates to reduced
convergence times; Secondly, low amounts of overlapping attack paths affects PBS more than PPM and TMS;
and thirdly, medium amounts of overlapping cause a drastic reduction in TMS convergence times. In the context
of a larger network, these results mean that even for long path lengths, the existence of common and therefore
overlapping attack paths translates to reduced convergence times for TMS and PBS more than it does for PPM.
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Fig 4. (a) TMS convergence times
Fig 4.(b) PBS
V. MAXIMUM ENTROPY RANDOM WALK
Instead of using GRW, Burda et al. [15] introduce the construction of Maximum Entropy Random Walk
(MERW), where the transition matrix is defined by the entries pi,j as follows.
pi,j =ai,j λ ψjψi
Fig 5.Entropy Random Walk
The term λ is the largest eigenvalue of A and ψ its corresponding normalized eigenvector withi ψ2 i =1. Thus,
the transition probabilities of the random walk process are similar to the eigenvector centrality of the nodes,
which is regarded as one way of describing the influence of a node within the topology. The same way of
defining entropy within a network and setting the transition probabilities accordingly to maximize entropy were
also discussed by Demetrius and Manke in [17]. They further establish a relationship between entropy and the
robustness of the average shortest path length since networks that have a higher entropy are also more robust
toward removal of nodes. The stationary distribution of finding a query packet at node i with MERW and its
entropy rate are then according to [15] as shown in Eqns. (6) and (7).π∗ i = ψ2 i (6) SMERW = logλ
(7)Unfortunately, the definition of the transition probabilities in Eqn. (5) requires knowledge of the largest
eigenvalue of the adjacency matrix and its corresponding eigenvector. This can only be determined if the
topology is fully known and is usually not very practical, especially in large networks. However, it was shown
by Sinatra et al. [16] that the maximum entropy random walk can be constructed only with limited and local
information based on the degrees of the first and second hop neighbors of each node.
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5.1 Average Number of Hops
The average number of hops for a query packet indicates the speed of search within the network. Figure 3 shows
the average hop count until finding one of the destinations for the three different methods with R = 50destination
nodes and the network size N varying from 200 to 1000. The results for the scale-free topology are shown in
Fig. 3(a) and for the small-world topology in Fig. 3(b).
Fig 5.(a) Scale-Free Topoloy
Fig5(b) Smal –world topology
VI. CONCLUSION AND FUTURE WORK
In this paper we studied the feasibility of applying a random walk query search in a data-centric network under
random and complex topologies. For suitable topologies, random walk does not necessarily perform much
worse than the commonly used flooding mechanism. On the contrary, since only a single path is followed during
the query search, fewer nodes are involved in the dissemination process, which leads to a lower consumption of
energy. We have seen that a maximum entropy random walk improves the general random walk in performance
by counteracting the irregularities in topology to balance the reachability probability of the destination nodes.
Furthermore, only a small number of destination nodes in the network is sufficient to provide replicas of the
desired content for achieving performance compared to flooding for both small world and scale-free topologies.
REFERENCES
[1]
C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: a scalable and robust communication
paradigm for sensor networks”, in Proc. 6th Annu. Int. Conf. on Mobile Computing and Networking
(MobiCom ’00), pp. 56–56, Boston, MA, 2000.
[2]
I. Stojmenovic´ and S. Olariu, “Data-centric protocols for wireless sensor networks”, in Handbook of
sensor networks: algorithms and architectures, I. Stojmenovic´ (Ed.), John Wiley & Sons, 2005.
[3]
V. Jacobson, D. K. Smetters, J. D. Thornton, M. Plass, N. Briggs, and R. Braynard. 2012. “Networking
named content”. Commun. ACM, vol. 55, no. 1, pp. 117–124, Jan. 2012.
[4]
J. Choi, J. Han, E. Cho, T. Kwon, and Y. Choi, “A survey on content-oriented networking for efficient
content delivery”, IEEE Com- mun. Mag., vol. 49, no. 3, pp. 121–127, March 2011.
[5]
A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for
habitat monitoring”, in Proc. 1st ACM Int. Workshop on Wireless Sensor Networks and Applications
(WSNA ’02), pp. 88–97, Atlanta, GA, September 2002.
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DISTRIBUTED RESTRAINING MALWARE
PROPAGATION IN MOBILE SINK ROUTING FOR
WIRELESS SENSOR NETWORKS
M.Sindhuja1, A.Komathi2
1
Mphil Scholar, Department of CS & IT, Nadar Saraswathi College of Arts and Science, (India)
2
Department of CS & IT, Nadar Saraswathi College of Arts and Science, (India)
ABSTRACT
Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost
sensor nodes with capability of sensing various types of physical and environmental conditions, data
processing, and wireless communication. Variety of sensing capabilities results in profusion of application
areas. However, the characteristics of wireless sensor networks require more effective methods for data
forwarding and processing. In WSN, the sensor nodes have a limited transmission range, and their processing
and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless
sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop
communication under these conditions. In this paper, we give a survey of routing protocols for Wireless Sensor
Network and compare their strengths and limitations.
Keywords: Wireless Sensor Networks, Routing Protocols, Cluster Head
I. INTRODUCTION
Wireless sensor network (WSN) is widely considered as one of the most important technologies for the twentyfirst century [1]. In the past decades, it has received tremendous attention from both academia and industry all
over the world. A WSN typically consists of a large number of low-cost, low-power, and multifunctional
wireless sensor nodes, with sensing, wireless communications and computation capabilities [2,3]. These sensor
nodes communicate over short distance via a wireless medium and collaborate to accomplish a common task,
for example, environment monitoring, military surveillance, and industrial process control [4]. The basic
philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate
power of the entire network is sufficient for the required mission. Although several defense mechanisms [6, 7]
have been pro- posed in the literature over the last few years, little work has been done to demonstrate
howvulnerable, in terms of data confidentiality and network availability, these networks are. Motivated by this
unexplored security aspect, wedemon- strate an attack tool that can be useful not only in highlight- ing the
importance of defending sensor networkapplications against attacks but also in studying the effects of these attacks on the sensor network itself. This in turn can lead to the development of more secure applications and
better detection/prevention mechanisms.
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II. ATTACK TOOL ARCHITECTURE OVERVIEW
The attack tool is based on an intelligent component-based system. The hosted components are capable of
monitoring any neighborhood traffic, decoding and logging overheard packets, constructing specially crafted
messages and launch- ing a number of attacks. Its core functionality is based on three main conceptual modules,
as depicted in Figure 1:
• A network sniffer for passive monitoring and logging of radio packets. Any network traffic analysis or packet
decoding can be done either in real time or offline through the implemented packet description database.
• A network attack tool that provides a number of actions for compromising a sensor network’s security profile.
It contains a data stream framework for constructing specially crafted packets that are transmitted by the attack
launcher throughout the duration of an attack.
• A network visualization component that visualizes and displays the neighborhood topology, network traffic,
node states and status of any performed attack.
The key design goal of this tool is its wide applicability; it should support passive inspection and compromise of
a wide variety of sensor network protocols and applications.
Figure 1: Attack Tool Architecture Layout.
III. NETWORK CONFIDENTIALITY THREATS AND WIRELESS ATTACKS
In wireless networking the overall security objectives remain the same as with wired networks: preserving
confidentiality, ensuring integrity, and maintaining availability of information. Thus, identifying risks to sensor
networks confiden- tiality posed by theavailability of transactional data is ex- tremelyvital.In an attempt to
identify network confidentiality threats, we enhanced our attack tool with a network sniffer for overhear- ing
network traffic (Section 3.1). In that way an adversary can process transmitted packets in order to extract vital
in- formation such as node IDs or traffic data. Our assertion is that traffic analysis can provide more information
about a network’snodes and usage than simply decoding any data packet contents. The presented tool can use
carrier frequency to launch a side-channel attack [18] in an attempt to identify the net- work’s sensor hardware
platform. An adversary could use either a spectrum analyzer or different sensor hardware in combination with
our tool in order to detect the current communication frequency. Once the adversary discovers it, she can
determine the hardware used and, thus, exploit all the protocol vulnerabilities arising from this specific platform. This tool can also compromise a network’s confidentiality by monitoring the rate and size of any
transmitted/received messages. Specifically, the message rate can reveal infor- mation about the network
application and the frequency of monitored events. This constitutes a severe threat since for some sensor
applications, like health monitoring, it can lead to a violation of user’s privacy. Furthermore, an adversary can
examine the rate at which she overhears messages com- ing from a neighborhood and estimate the distance to
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the sensed event. Research has shown that the message recep- tion rate increases when the distance to the event
reporting node decreases.
Fig 2. Transactions Vector
3.1 Network Sniffer Component
The network sniffer relies on packets that are overheard in a sensor’s node neighborhood. It captures them and
logs them for later analysis. Conceptually the sniffer consists of a Local Packet Monitoring module for gathering
audit data to be forwarded, over its serial port, to the Packet Storage module for logging at the attached host.
This allows offline analysis, through the Packet Description Database, in order to extract vital network
information such as node IDs, traffic data or used protocol versions. Essentially, the sniffer enables the
construction of a directed graph of all neighboring nodes. Overheard packets flow along the edges of the graph,
as
Fig 3. Wireless Network Sniffer
Audit data consist of the communication activities within the sniffer’s radio range. Such data can be collected by
lis- tening promiscuously to neighboring nodes’ transmissions. By promiscuously we mean that when a node is
within ra- dio range, the local packet monitoring module can overhear communications originating from that
node. Once captured by the radio, all packets are timestamped in order to facili- tate subsequent time-based
analysis. Timestamping is per- formed the moment the packet is received by the network sniffer.
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3.2 Network Attack Tool Component
This component core functionality is to provide a number of actions for compromising the sensor network’s
security pro- file. After gathering audit data that are used by the network sniffer to extract vital information and
identify the used sen- sor hardware platform and underlying protocols, a user can start launching a number of
attacks
Fig 3.2 (a) Hardware Platform Protocoal
Fig 3.2 (b) List of Supported at- Tacks Can be Found
The resulting network information stream from the packet decoder is fed to the Data Stream Framework of the
attack tool component. This data stream processor uses the identi- fied carrier frequency, message size and
routing information as its configuration record. All these network characteristics are essential since they are used
as the basis for any specially crafted message required by the Attack Launcher.
IV. IMPLEMENTED ATTACKS & ACTIONS
Many sensor network deployments are quite simple, and for this reason they can be even more susceptible to
attacks. What makes it particularly easy for attackers is the fact that most protocols are not designed having
security threats in mind. As a consequence, they rarely include securityprotection and little or no effort is usually
required from the side of an adversary to perform an attack. So, it is very important to study realistic attacker
models and evaluate their practicality and effectiveness through a tool as the one presented in this work.
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The nature of wireless network communications opens the way to four basic attacks: Interception, Alteration,
Disrup- tion and Code or Packet Injection [5]. Most network layer attacks against such networks fall into one of
these cate- gories. Our attack tool (in its current version) gives the user the opportunity to perform, in addition to
eavesdrop- ping and sniffing, the following actions:
V. CONCLUSIONS
In this paper, we have identified some of the sensor net- works vulnerabilities that can be exploited by an
attacker for launching various kinds of attacks. We have demonstrated the practicality of these attacks by
building an attack tool for compromising the network’s confidentiality and function- ality. The results of this
work serve a three-fold purpose: to reveal the vulnerabilities of such networks, to study the ef- fects of severe
attacks on the network itself and to motivate a better design of security protocols that can make them more
resilient to adversaries. Wireless sensor network security is an important research direction and tools like the
current one may be used in coming up with even more attractive solutions for defending these types of
networks.
REFERENCES
[1]
A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson. Wireless sensor networks for
habitat monitoring. In WSNA ’02: Proceedings of the 1st ACM international workshop on Wireless
sensor networks and applications, pages 88–97, New York, NY, USA, 2002. ACM.
[2]
J. Tateson, C. Roadknight, A. Gonzalez, T. Khan, S. Fitz, I. Henning, N. Boyd, C. Vincent, and I.
Marshall. Real World Issues in Deploying a Wireless Sensor Network for oceanography. In Workshop on
Real-World Wireless Sensor Networks REALWSN’05, Stockholm, Sweden, June 2011.
[3]
D. Trossen, D. Pavel, G. Platt, J. Wall, P. Valencia, C. A. Graves, M. S. Zamarripa, V. M. Gonzalez, J.
Favela, E. Livquist, and Z. Kulcs Sensor networks, wearable computing, and healthcare applications.
IEEE Pervasive Computing, 6:58–61, 2007.
[4]
A. Becher, Z. Benenson, and M. Dornseif. Tampering with motes: Real-world physical attacks on
wireless sensor networks. In J. A. Clark, R. F. Paige, F. Polack, and P. J. Brooke, editors, SPC, volume
3934 of Lecture Notes in Computer Science, pages 104–118. Springer, 2006.
[5]
C. Karlof and D. Wagner. Secure routing in wireless sensor networks: Attacks and countermeasures.
AdHoc Networks Journal, 1(2–3):293–315, September 2003.
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PRESERVING CLOUD CONSISTENCY USING CAAS
MODEL
P. Malaimari Suganya1 , R.Padmapriya2
1
Mphil Scholar, 2Assistant Professor, Department Of Computer Science ,
Nadar Saraswathi College (India)
ABSTRACT
Cloud storage services are commercially more popular due to their amount of advantages. Most of the cloud
service provider provides services like infrastructure management, data storage services on 24/7 through any
devices at anywhere. To provide this ubiquitous always on service most of the cloud service provider (CSP)
maintains each piece of data on geographically distributed servers. The main key problem with this technique is
that, it is very expensive and some to fail to provide required consistency of service. To overcome this problem,
we propose to use a new approach of service (i.e. Consistency as a Service(CaaS)) this paper, firstly
concentrate on a consistency as a service (CaaS) model, which has a large data cloud and multiple small audit
clouds. In the CaaS model, a data cloud is formed by a CSP, and a group of users form an audit cloud that can
verify whether the data cloud provides the promised level of consistency i.e. quality of service or not, for that
make use of two-level auditing strategy which require loosely synchronized clock for ordering operations in an
audit cloud. Then perform global auditing by global trace of operations through randomly electing an auditor
from an audit cloud. Finally, use a heuristic auditing strategy(HAS) to display as many violations as possible.
Keywords: Cloud Storage Systems, Consistency As A Service (Caas), Two-Level Auditing and
Heuristicauditing Strategy
I. INTRODUCTION
Clouds computing is become more popular as it provides guaranteed services like data storage, virtualized
infrastructure etc. e.g. Amazon,SimpleDB etc. By using the cloud services, the customers or user can access
data stored in a cloud anytime and at anywhere using any device, and customer ensure about less capital
investment. To provide promised always on 24/7 access, the cloud service provider (CSP) stores data replicason
multiple geographically distributed servers. The main drawback of using the replication technique is it is very
expensive to achieve strong consistency, and user is ensured to see the latest updates. Many CSPs (e.g., Amazon
S3) provide only eventual i.e. updates are visible definitely but not immediately. E.g. Domain name system
(DNS), but the eventual consistency is not interesting for all applications and which require strong consistency.
Some applications like social networking sites require causal i.e. strong consistency. Thus the different
applications require different level of consistency. We propose novel consistency as a service (CAAS) model.
The CaaS model consists of, A large data cloud formed by CSP and multiple audit clouds formed by group of
users worked on project or document that can check whether the data cloud provide a promised level of
consistency or not. Two-level auditing structure which require only a loosely synchronized clock for ordering
operation in an audit cloud then perform global auditing with a global trace of operations periodically an auditor
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is elected from an audit cloud. Local auditing is concentrate on monotonic-read and read-your-write
consistencies, which can be performed by an online light-weight algorithm while Global auditing focuses on
causal consistency, in which construct a directed graph. If the constructed graph is a directed acyclic graph also
called as precedence graph, we claim that causal consistency is preserved. We determine the severity of
violations by two metrics for the CaaS model: commonality of violations and staleness of the value of a read, as
in. Finally, we propose a heuristic auditing strategy (HAS) which adds appropriate reads to display as many
violations as possible to determine cloud consistency and also actual cost per transaction.
II. DESCRIPTION
This section consist of three models i. e. consistency as a service (CaaS) model, user operation table (UOT) with
which each user records his operations and two-level auditing structure.
2.1 Consistency as a Service (CAAS) Model
An audit cloud consists of a group of users that work together on a job, e.g., a document or a program. We
consider that each user in the audit cloud is identified by a unique ID. Before assigning job to the data cloud, an
audit cloud and the data cloud will engage with a service level agreement (SLA), which demands the promised
level of consistency should be provided by the data cloud. The audit cloud exists to verify whether the data
cloud violates the SLA or not, and to analyze the severity of violations.
2.2 User Operation Table (UOT)
Each user maintains his own User Operation Table (UOT) for recording his trace of operations. Each record in
the UOT is described by elements like Operation, logical vector,
and physical vector. While issuing an operation, a user from an audit cloud will record his operation in UOT, as
well as his current logical vector and physical vector. Each user will maintain a logical vector and a physical
vector to track the logical and physical time when an operation happens, respectively.
2.3 Two-Level Auditing Structure
2.3.1 Local Auditing
Each user independently performs local auditing with his UOT with two consistencies; Monotonic-read
consistency, which requires that a user must read either a new value or same value Read-your’s-write
consistency, which require a user, always read his latest update.
2.3.2 Global Auditing
Global auditing is performed by global trace of operations of all users operations with following consistency
Causal Consistency Causal consistency writes that are causally related must be seen by all process in the same
order and concurrent writes may be seen in a different order on different machine.
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International Conference on Emerging Trends in Technology, Science and Upcoming Research in Computer Science
DAVIM, Faridabad, 25th April, 2015
ISBN: 978-81-931039-3-7
Fig:1 Consistency as a Service Model
2.4 Heuristic Auditing Strategy
From the auditing process it is clear that only reads can display violations by their values. Therefore, the basic
idea behind the heuristic auditing strategy (HAS) is to add exact reads for displaying as many violations as
possible and call these additional reads as auditing reads. Under the CaaS model, consistency becomes a part of
the Service Level Agreement and the users can get something from the CSP, by displaying consistency
violations and determine the severity of the violations. The CaaS model will help both the CSP and the users
adopt consistency as an important aspect of cloud services.
III. CONCLUSION
In this paper, we argued that strong consistency requirements should be adopted only for data objects
crucial for application correctness, otherwise weaker forms of data consistency should be adopted. We
presented aconsistency as a service (CaaS) model and a two-level auditing structure that helps users to
verify whether the cloud service provider (CSP) is providing the promised consistency, and to
quantify the severity of the violations, if any. With the CaaS model, the users can assess the quality of
cloud services and choose a right CSP among various candidates, e.g., the least expensive one that
still provides adequate consistency for the users’ applications.
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in Liu, Guojun Wang, IEEE, Member, and Jie Wu, IEEE Fellow “Consistency as a service:
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DAVIM, Faridabad, 25th April, 2015
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