A weighted-dissimilarity-based anomaly detection method for mobile networks

  • Authors:
  • Hwa-Ju Lee;Kyung-Sook Lee;Ihn-Han Bae

  • Affiliations:
  • Catholic University of Daegu, Gyeongsan, Gyeongbuk, Republic of Korea;Catholic University of Daegu, Gyeongsan, Gyeongbuk, Republic of Korea;Catholic University of Daegu, Gyeongsan, Gyeongbuk, Republic of Korea

  • Venue:
  • Proceedings of the 2009 International Conference on Hybrid Information Technology
  • Year:
  • 2009

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Abstract

Mobile wireless networks continue to be plagued by theft of identity and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a weighted-dissimilarity-based anomaly detection method that can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, the dissimilarity is computed by a weighted dissimilarity measure. If the computed dissimilarity value is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of anomaly detection methods using other dissimilarity measures.