This paper addresses the problem of efficient intrusion detection for mobile devices via correlating the user's location and time data. We developed two statistical profiling approaches for modeling the normal spatio-temporal behavior of the users: one based on an empirical cumulative probability measure and the other based on the Markov properties of trajectories. An anomaly is detected when the probability of a particular (location, time) evolution matching the normal behavior of a given user becomes lower than a certain threshold, determined by controlling the recall rate of the model of the normal user's behavior. We used compression techniques to reduce processing overhead while maintaining high accuracy. Our evaluation based on the Reality Mining and Geolife data sets shows that the proposed system is capable of detecting a potential intrusion within 15 min and with 94 % accuracy.
- Data reduction
- Mobile security
- Trajectory analysis
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Science Applications
- Management Science and Operations Research