Using Near-Realtime Voice Data for Communication Authentication Andreas Brauchli, and Depeng Li University of Hawai‘i at Mānoa 1. Establish communication Problem (insecure) ... 2. Estimate link quality ... (latency, jitter) Channel latency Impostor Public common history Decision-error implications Creating an authentic channel without an authenticated Public Key Tools Challenges 3. Recognize voice ... (anomalies, tension) 4. Challenge - Response ... ! Insecure channel Known voice known behavior Common history Human decider Public key cryptography (until convinced) ... ? 5. Describe Public Key 6. Get, verify and use PK (ﬁngerprint) Human Decider ... Realtime Aspect Important to assess authenticity: Reaction to challenge questions Realistic impersonation much harder Multi-Hop link latency ... Generally harder to fool Intuition Tolerance Decision is not binary Implementation AllNet (future) Fig. 1 One-sided protocol illustration Conclusions Humans are inherrently good at recognizing voices, behavior and detecting anomalies. This valuable resource should always be considered when developing authentication systems. https://www.alnt.org Abstract: https://www2.hawaii.edu/~andreasb/ pub/poster_realtime-voice-auth.pdf Contact: [email protected] [email protected]