Attack classification based on data mining technique and its application for reliable medical sensor communication

Detecting network intrusion has been not only important but also difficult in the network security research area. In Medical Sensor Network(MSN), network intrusion is critical because the data delivered through network is directly related to patients' lives. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics in MSN. Therefore, unsupervised learning techniques such as SOM (Self-Organizing Map) are more appropriate for anomaly detection. In this paper, we propose a real-time intrusion detection system based on SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features. We experiments our system with KDD Cup 1999 data set because MSN data is not available yet. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack.