ePet: When Cellular Phone Learns to Recognize Its Owner
Format of Original
Association for Computing Machinery
Proceedings of the 2nd ACM Workshop on Assurable and Usable Security Configuration
Original Item ID
In this paper an adaptive solution to secure the authentication process of cellular phones has been proposed. Gait and location tracks of the owner are used as the metrics for authentication. The cellular phone is envisioned to become as adaptive as a pet animal of the owner. The cellular phone learns various intrinsic attributes of the owner like his voice, face, hand and fingerprint geometry and interesting patterns in the owner's daily life and remembers those to continually check against any anomalous behavior that may occur due to the stealing of the phone. The checking is done level wise. Higher level of authentication is more stringent. Only when the cellular phone recognizes significant anomaly in a lower level, it goes one level up in the security hierarchy. The iPhone's accelerometer and A-GPS module have been utilized to record gait and location signatures. A fast and memory efficient variation of Dynamic Time Warping (DTW) algorithm called FastDTW has been used to compute the similarity score between gait samples.
Tanviruzzaman, Mohammad; Ahamed, Sheikh Iqbal; Hasan, Chowdhury Sharif; and O’brien, Casey, "ePet: When Cellular Phone Learns to Recognize Its Owner" (2009). Mathematics, Statistics and Computer Science Faculty Research and Publications. 323.