Towards Accountable Mobility Model: A Language Approach on User Behavior Modeling in Office WLAN

摘要

Modeling mobile users' behavior can lead to crucial applications in accountable mobile computing such as casual authentication and anomaly detection. We introduced a language approach to model mobile users' behavior from heterogeneous sensor data. By converting temporal and spacial features generated from WiFi RSS trace into symbols and fusing them into a 1-dimension ``language'' representation, we were able to leverage algorithms developed for statistical NLP to build accountable user mobility models in an office WLAN environment. We explored the continuous n-gram and skipped n-gram models to detect anomaly of mobile user's behavior, such as device theft. We have collected data from network infrastructure in an corporate office environment over 5 days. The proposed model only needs to observe the users for 8 hours to build a reliable behavior model which can detect 86% of device theft cases. We have also evaluated the effectiveness of using the n-gram models to predict the future location of the user.