Design and Evaluation of a Rough Set Based Anomaly Detection Scheme Considering the Age of User Profiles

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.