Design and Evaluation of a Rough Set-Based Anomaly Detection Scheme Considering Weighted Feature Values

  • Ihn-Han Bae
  • Hwa-Ju Lee
  • Kyung-Sook Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


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.


False Alarm Rate Intrusion Detection Anomaly Detection Weighted Feature Deviation Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ihn-Han Bae
    • 1
  • Hwa-Ju Lee
    • 1
  • Kyung-Sook Lee
    • 1
  1. 1.School of Computer and Information Communication Eng.Catholic University of DaeguGyeongSanKorea

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