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

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.