The effects of Artificial Intelligent on online Intrusion Detection System

Abstract

The intelligent intrusion detection system is an interesting approach which is designed to protect host/network systems from the potential attacks. The intrusion detection system is the most important techniques that were used to improve system security and reduce the number of attacks. In our paper, we propose an intelligent intrusion detection system (IDS) to improve the detection rate and decline the false alarms that generated from the proposed detection system. The detection process based on KDD - Cup 2000 benchmark data that collected by Defense Advanced Research Projects Agency (DARPA). In other word, the detection system depends on the features that described the normal/ abnormal behavior of the network /host system as well as we used a significant feature which reflected behavior in real–world. The online detection is considered main contribution in our proposal security system. The proposed IDS utilize soft computing techniques such as Self-Organizing Map (SOM) and Backpropagation neural network. The experimental result shows the efficiency and effectiveness of the proposed system in the protection and deterrence.