INTRUSION DETECTION USING ARTIFICIAL NEURAL NETWORK WITH REDUCED INPUT FEATURES

Intrusion Detection is the task of detecting, preventing and possibly reacting to intrusion in a network based computer systems. This paper investigates the application of the Feed Forward Neural Network trained by Back Propagation algorithm for intrusion detection. Mutual Information based Feature Selection method is used to identify the important features of the network. The developed network can be used to identify the occurrence of various types of intrusions in the system. The performance of the proposed approach is tested using KDD Cup’99 data set available in the MIT Lincoln Labs. Simulation result shows that the proposed approach detects the intrusions accurately and is well suitable for real time applications.