An Improved Intrusion Detection Technique based on two Strategies Using Decision Tree and Neural Network

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection System (IDS). By applying DT known attacks would be recognized with a quick execution time. Unknown attacks would be detected by applying the unsupervised NN based on hybrid of Self Organizing Map (SOM) for clustering attacks into smaller categories and supervised NN based on Backpropagation for detailed clustering.