Enhancing security using mobility-based anomaly detection in cellular mobile networks
Date
2006-07
Authors
Sun, Bo
Yu, Fei
Wu, Kui
Xiao, Yang
Leung, Victor C.M.
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Location information is an important feature in users' profiles in cellular mobile networks. In this paper, by exploiting the location history traversed by a mobile user, two domain-independent online anomaly detection schemes are designed, namely the Lempel-Ziv (LZ)-based and Markov-based detection schemes. The authors focus on the identification of a group of especially harmful internal attackers-masqueraders. For both schemes, cell IDs traversed by each mobile user are extracted as the feature value. Specifically, the mobility pattern of each user is characterized by a high-order Markov model. The LZ-based detection scheme from the well-developed data compression techniques is derived. Moreover, the technique of exponentially weighted moving average is used to modify a user's normal profile dynamically. The user profile can characterize the normal behavior of each user accurately and is sensitive to abnormal changes. For the Markov-based detection scheme, a fixed-order Markov model is used to characterize the normal behavior. Based on the constructed probability transition matrix, the probability of the user's current activity is calculated. A threshold policy is then used in both schemes to determine whether a mobile device is potentially compromised or not. Simulation results are presented to show the effectiveness of the proposed schemes. Moreover, our results show that the LZ-based detection scheme performs better than the Markov-based detection scheme, especially for low-speed mobile users
Description
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Keywords
Citation
Bo Sun; Fei Yu; Kui Wu; Yang Xiao; Leung, V.C.M.; , "Enhancing security using mobility-based anomaly detection in cellular mobile networks," Vehicular Technology, IEEE Transactions on , vol.55, no.4, pp.1385-1396, July 2006