Design and evaluation of a rough set-based anomaly detection scheme considering weighted feature values

Authors:
Ihn-Han Bae;Hwa-Ju Lee;Kyung-Sook Lee
Affiliations:
School of Computer and Information Communication Eng., Catholic University of Daegu, GyeongSan, Korea;School of Computer and Information Communication Eng., Catholic University of Daegu, GyeongSan, Korea;School of Computer and Information Communication Eng., Catholic University of Daegu, GyeongSan, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 3
Cited 1

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Abstract

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 use 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 considering weighted feature values. The performance of our scheme is evaluated by a simulation.