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

Issue title: Extended papers selected from KES-2006

Article type: Research Article

Authors: Bae, Ihn-Han^{; *} | Lee, Hwa-Ju | Lee, Kyung-Sook

Affiliations: School of Computer and Information Communication Engineering, Catholic University of Daegu, GyeongSan 712-702, Korea

Correspondence: [*] Corresponding author. Tel.: +82 850 2742; Fax: +82 850 2740; E-mail: [email protected]

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

DOI: 10.3233/KES-2007-11402

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 11, no. 4, pp. 201-206, 2007