ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
IWA: IDENTIFYING NUMEROUS WANGLING
ATTACKERS IN WIRELESS NETWORKS
Hema M
Natchadalingam R
ME CSE, PSN Engineering College, Tirunelveli,
Tamilnadu, India
Professor Dept of CSE, PSN Engineering College,
Tirunelveli, Tamilnadu, India
Abstract: Wireless wangling attacks are easy to
commonly available platforms to launch a variety
of attacks with tiny effort. According various types of
attacks, identity-based wangling attacks are
especially easy to launch and can cause significant
damage to network. Wangling attacks can further
facilitate a variety of traffic injection attacks [2], [3],
such as attacks on access control lists, scoundrel
access point attacks, and eventually Denial-ofService (DoS) attacks. A broad survey of possible
wangling attacks can be found in [4], [5]. Moreover,
in a large-scale network, numerous antagonists may
masquerade as the same identity and collaborate to
launch malicious attacks such as network resource
utilization attack and denial-of-service attack quickly.
start on and can significantly impact the
performance of networks .This paper proposes to
use spatial information, a substantial property
associated with every node, hard to fake .and not
dependent on cryptography, as the basis for
1.identifying wangling attacks; 2.establishing the
number of attackers when numerous antagonists
hidden as a same node identity: 3.deliberate
diverse antagonists. The spatial correlation of
received signal strength inherited from wireless
nodes are used to identifying the wangling attacks.
Then the problem of determining the number of
attackers as multiclass finding problem is
formulated.
Cluster-based
mechanism
is
developed to start the number of attackers. if the
instruction data is available, Support Vector
Machines (SVM) method is used to further
improve the accuracy of determining the number
of attackers and also integrated identification and
position system is used concentrate the positions of
numerous attackers.
Keywords: Wireless network security, wangling
attack, attack identification
I. INTRODUCTION
In wireless networks the openness of
transmission medium, attackers can observe any
transmission. Further, these attackers can easily
purchase low-cost wireless devices and use these
Therefore, it is important to



Identifying the presence of wangling attacks,
Establish the number of attackers, and
Concentrate numerous antagonists and
eliminate them.
Most
existing
approaches
employ
cryptographic schemes to address probable wangling
attacks [7], [6].However, the
application
of
cryptographic schemes requires reliable key
delivery
management,
and
continuation
mechanisms. It is not always enviable to apply
these cryptographic methods because of its
computational , infrastructural ,and management
transparency. Advance cryptographic methods are
liable to node negotiation, which is a severe concern
as most wireless nodes are without problems
available, allowing their memory to be without
problems scanned. This paper proposes to use RSS-
32
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
based spatial correlation, a substantial property
associated with each wireless node that is hard to
fake and not dependent on cryptography as the basis
for identifying wangling attacks. Since the concern is
on the attackers who have different locations than
legitimate wireless nodes, utilizing spatial in
sequence to address wangling attacks has the unique
power to not only identify the presence of these
attacks but also concentrate antagonists An added
advantage of employing spatial correlation to
identifying wangling attacks is that it will not
require any additional cost or modification to the
wireless devices themselves.
The focal point is on static nodes in this
work, which are frequent for wangling scenario [8].
The works that are strongly connected are [4], [8],
[10]. [4] Proposed the use of matching rules of signal
print for wangling exposure [8] model the RSS
readings using a Gaussian mixture model and [10]
used RSS and K-means cluster analysis to identifying
wangling attacks. However, none of these approaches
have the ability to establish the number of attackers
when numerous antagonists use a same identity to
launch attacks, which is the basis to further
concentrate numerous antagonists after attack
detection. Although [10] studied how to
Concentrate antagonists , it can only handle the
case of a single wangling attacker and cannot
Concentrate the attacker if the adversary uses
different transmission power levels.
The main aid of the work are:


GAIE : a generalized attack identification
model that can both identifying wangling
attacks as well as establish the number of
antagonists using cluster analysis methods
grounded on RSS-based spatial correlations
among normal devices and antagonists
IIPS: an integrated identification and
position system that can both identifying
attacks as well as find the positions of
numerous antagonists
even when the
antagonists vary their transmission power
levels.
In GAIE, the Partitioning Around Medoids
(PAM) cluster analysis method is used to perform
attack detection. The problem of determining the
number of attackers as a multi-class detection
problem is formulated. Then cluster based methods
are applied to establish the number of attacker. IIPS
mechanism to make existing minimum reserve of
clusters, to progress the exactness of seminal the
number of attackers. Additionally, if the instruction
data is available, Support Vector Machines (SVM)
method is used to further improve the accuracy
of establishing the number of attackers. Moreover,
an integrated system, IIPS, is used which utilizes the
results of the number of attackers returned by GAIE
to further Concentrate numerous antagonists
The rest of this paper is prepared as follows.
In Section II, the related work is review; Overview of
the techniques is presented in Section III. The future
scheme is described in Section IV. In Section V,
algorithm is conducted. Section VI provides
performance analysis of the proposed scheme. We
conclude in Section VII.
II. RELATED WORK
The traditional approach to prevent
wangling attacks is to use cryptographic-based
authentication [7],[6], [11]. Wu et al. [7] have
introduced a secure and efficient key management
(SEKM) framework. It build a Public Key
Infrastructure (PKI) by applying a secret sharing
scheme and an underlying multicast server group.
Wool [7]implemented
a
key
management
mechanism with periodic key refresh and host
revocation
to
prevent
the compromise of
authentication keys.
In wireless networks new approaches
utilizing substantial properties associated with
wireless broadcast to contest attacks have been
proposed. Based on the fact that wireless control
response decelerates quite quickly in space, a
33
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
channel-based confirmation scheme was proposed
to discriminate between transmitters at different
locations, and thus to identifying wangling attacks
in wireless networks [12].Brik et al. [13] focused
on construction fingerprints of 802.11bWLAN
Network Interface Card by dig out radiometric
signature, such as frequency extent phase errors,
and I/Q derivation offset, to defend against
identity attacks. However, there is further
transparency associated with wireless channel
response and radiometric signature extraction in
wireless networks. Li and Trappe [6] introduced a
security
layer
that
used
forge-resistant
relationships based on the package interchange,
including MAC progression number and traffic
pattern, to identify wangling attack. The MAC
sequence number has also been used in [14] to
perform wangling detection. Both the sequence
number and the traffic pattern can be
manipulated by an adversary as long as the
adversary learns the traffic pattern under normal
conditions.
with ranging method, range-based algorithms
involve distance estimation to landmarks using the
measurement of various substantial properties
such as RSS [16], [15], Time Of Arrival (TOA)
, Time Difference Of Arrival (TDOA), and route of
arrival (DoA). Whereas range-free algorithms use
coarser metrics to place limits on candidate positions.
one more method of classification describes the
strategy used to map a node to a position Lateration
approach use detachment to landmark, if angulations
uses the angles from landmarks. Scene matching
strategies [16] use a function that maps observed
radio properties to locations on a pre constructed
signal map or database and additional, Chen
proposed to perform detection of attacks on wireless
positioning and Yang proposed to use the direction of
arrival and received signal strength of the signals to
concentrate enemy’s sensor nodes and also we
choose a group of algorithms employing RSS to
perform the task of concentrate numerous attackers
and evaluate their performance in terms of
positioning accuracy
The works [4], [8], [15] using RSS to defend
against WANGLING attacks are most closely related
to us. Faria and Cheriton [4] proposed the use of
matching rules of signalprints for wangling detection.
Sheng et al. [8] modelled the RSS readings using a
Gaussian mixture model. Sang and Arora [15]
proposed to use the node’s “spatial given name”
including Received Signal Strength Indicator
(RSSI)and Link Quality Indicator (LQI) to confirm
messages in wireless network. However, not any of
these approaches are talented of determining the
number of attackers when there are numerous
antagonists collaborating to use the same identity to
launch spiteful attack and additional they do not have
the ability to concentrate the positions of the
antagonists after attack detection
This work differs from the previous study in
that here the spatial information is used to assist in
attack detection instead of relying on cryptographicbased approaches and moreover, this work is novel
because none of the exiting work can establish the
number of attackers when there are numerous
antagonists masquerading as the same uniqueness.
furthermore
this
approach
can
accurately
Concentrate numerous antagonists even when the
attackers varying their transmission power levels to
trick the system of their true locations.
Turning to studying identifying techniques,
in spite of its several meter-level exactness, using
RSS [16], [15], is an attractive approach because it
can reuse the existing wireless infrastructure and is
highly correlated with substantial location. Dealing
III. OVERVIEW OF TECHNIQUES
(1)Generalized attack identifation model
Generalized Attack Identification Model
(GAIE) consists of two phases: attack finding, which
finds the presence of an attack, and number purpose,
which establishes the number of antagonists.
(2) Formative the number of attackers
34
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
Inaccurate estimation of the number of
attackers will cause failure in concentrate the
numerous antagonists. Since it is not known that how
many antagonists will use the same node identity to
launch attacks, formative the number of attackers
becomes a multi-class finding problem and is similar
to determining how many clusters exist in the RSS
readings.
3.
4.
5.
(3) IIPS:
Integrated identification and position
system integrated systems that can identifying
wangling attacks, establish the number of
attackers, and concentrate numerous antagonists .
6.
The number of attackers: The estimation of
the number attackers will cause failure in the
same node identity
Attacker number determination: The system
uses the twin cluster model, that can use
energy calculation
IIPS mechanism: The system can
identifying wangling attacks, establish the
number of attackers. that employs the
minimum distance testing in addition to
cluster analysis to achieve better accuracy of
determining the number of attackers
SVM mechanism: used to further improve
the accuracy of determining the number of
attackers present in the system.
IV. PROPOSED SYSTEM
The proposed system uses received signal
strength (RSS)-based spatial correlation, a
extensive property associated with each wireless
node that is hard to fake and not dependent on
cryptography as the basis for identifying wangling
attacks. Since the concern is on the attackers who
have different positions than justifiable wireless
nodes, utilizing spatial in sequence to address
wangling attacks has the unique power to not only
classify the presence of these attacks but also
concentrate antagonists. An employ spatial
correlation to identifying wangling attacks is that it
will not require any additional cost or adjustment to
the wireless devices themselves.
The system implementation involves the following
modules:
1.
2.
Handling different transmission: The
wangling attacker used transmission power
of 10dB to send packets where as original
node used 15dB transmission power level
Performance detection: The results are
encouraging showing for fake positive rates
less than 10 percent, the finding rate are
above 98 percent when the threshold is
around 8dB
V. ALGORITHMS
In order to estimate the generality of
IIPS for contemplate antagonists,a set of agent
positions algorithms range from next-door neighbor
matching in signal space(RADAR ), to probabilitybased (Area-Based Probability ), and to
multilateration (Bayesian Networks) are chosen.
5.1 RADAR-Gridded:
The RADAR-Gridded algorithm is a scene-matching
position algorithm. RADAR-Gridded uses an
interpolated gesture plan, which is built from a set of
averaged RSS readings with known (x, y) positions.
Given an experimental RSS reading with an unknown
position, RADAR returns the x, y of the next-door
neighbor in the signal map to the one to concentrate,
where "adjacent" is defined as the Euclidean distance
of RSS points in an N-dimensional gesture space,
where N is the number of landmark.
5.2 Area Based Probability (ABP):
ABP also expenditure an interpolated signal
map and advance the experimental area is separated
into a regular grid of identical sized tiles. ABP
assume the division of RSS for each landmark
follows a Gaussian distribution with mean as the
expected value of RSS interpretation vectors. ABP
35
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFER
CONFERENCE ON RECENT ADVANCES
CES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
then computes the probability of the wireless device
being at each tile Li, with i =1...L,
1...L, on the floor using
5.2.1 Bayes’ rule:
(1)
Particular that the wireless join must be at
exactly one tile satisfying Pi=1 ABP normalizes the
probability and returns the most likely tiles/grids up
to its confidences
5.3 Bayesian Networks (BN):
BN position is a multi lateration algorithm
that encodes the signal-to-distance
distance propagation model
into the Bayesian Graphical Model for localization.
The vertices X and Y represent location; the
vertex si is the RSS reading from the ith
landmark; and the vertex Di presents the Euclidean
distance between the location specified by X and Y
and the ith landmark.
Fig 1:: Network Simulation for proposed scheme
6.2 Attacker Detection
VI.PERFORMANCE ANALYSIS
6.1. Simulation Environment
Our model is based on the PHY and MAC
layer of the IEEE 802.11b, which is iincluded in the
NS2.The transport protocol is User Datagram
Protocol (UDP). Traffic sources are Constant Bit
Rate (CBR). The number of nodes is equally
distributed over the entire network.
In Figure 2 show that the simulation result of the
proposed scheme
Fig 2: Attacker Detection in networks
Figure 2shows
shows the relationship between the
Attacker Detection in networks. Here the network
consists of the number of nodes associated with the
Router. Clearly we can observe that the attackers’
decreases in our scheme, the lowest attackers attack
is yielded.
6.3 Throughput
36
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFER
CONFERENCE ON RECENT ADVANCES
CES IN COMMUNICATION SYSTEMS
AND TECHNOLOGIES
(ICRACST’15)
TH
25 MARCH 2015
In our future work, we will expand our idea
about congestion control mechanism with routing to
decrease the attackers attack and test it by simulation
REFERENCES
Fig 3:: Throughput with time interval
Figure 3 shows the connection between the
throughput and the time interval. As the time
increases the system attains the maximum throughput
value.
VII. CONCLUSION
This work, proposed to use received
signal strength (RSS) based spatial connection, a
corporeal property associated with every wireless
device that is hard to falsify and not reliant on
cryptography as the basis for identifying wangling
attacks in wireless networks. This approach can both
identifying the presence of attacks as well as
establish the number of antagonists , wangling the
same node identity, so that any number of attackers
can be concentratedd and can eliminate them.
Determining the number of antagonists is a
particularly challenging problem. This paper uses
IIPS mechanism that employs the minim
minimum distance
testing in addition to cluster analysis to achieve better
accuracy of determining the number of attackers
than. Furthermore, when the instruction data is
available, Support Vector Machines (SVM) based
mechanism is used to further improve the ac
accuracy of
determining the number of attackers present in the
system.
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