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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.

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