IJCTT-International Journal of Computer Trends and Technology
Data mining methods have gained importance in addressing computer network security. Existing rule based classification models for anomaly detection is ineffective in dealing with dynamic changes in intrusion patterns and characteristic. Unsupervised learning methods have been given a closer look for network anomaly detection. The authors investigate hierarchical clustering algorithm for anomaly detection in wireless LAN traffic. Since there is no standard datasets available to do research in wireless network, they simulated a wireless LAN using NS-2 and the traces are used to observe the traffic patterns.