Network Intrusion Detection using Hybrid Neural Networks

Abstract

Intrusion detection is a critical process in network security. It is the task of detecting, preventing and possibly reacting to the attack and intrusions in a network based computer systems. This paper presents an intrusion detection system based on self-organizing maps (SOM) and back propagation network (BPN) for visualizing and classifying intrusion. The performance of the proposed hybrid neural network approach is tested using KDD cup' 99 data available in the UCI KDD archive. The proposed approach considers all kinds of attacks under major category (normal, DOS, probe,U2R, and R2L) which provides an insightful visualization for network intrusion and works well in detecting different attacks in the considered system