With the increase in popularity of mobile devices, there has been a significant rise in mobile related security problems. The biggest threat for a mobile subscriber is lost or stolen device, which can lead to confidential data leakage, identity theft, misuse, impersonation, and high service charges. A significant amount of time may elapse between losing a device and disabling it through the service provider, during which an unauthorized malicious user may gain access and incur severe damage. We propose a probabilistic approach to spatio-temporal anomaly detection and evaluate smoothing techniques for sparse data. Our approach outperforms Markov Chain in experiments with a mobile phone dataset comprising over 500,000 hours of real data. Results indicate that our approach can effectively and efficiently detect device abnormalities for location, time, or both.
Tandon, G., Chan, P.K. (2007). Spatio-temporal anomaly detection for mobile devices (CS-2007-02). Melbourne, FL. Florida Institute of Technology.