Intrusion Detection System Using Back Propagation Algorithm and Compare its Performance with Self Organizing Map

Abstract

In recent years, internet and computers have been utilized by many people all over the world in several fields. On the other hand, network intrusion and information safety problems are ramifications of using internet. In this thesis it propose a new learning methodology towards developing a novel intrusion detection system (IDS) by back propagation neural networks (BPN) and self organizing map (SOM) and compare the performance between them. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviors of users, and then these behaviors will be considered an attack or a normal behavior. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors and unsupervisors analyze, model and understand the complex attack behavior of electronic crime.

Journal of Advanced College of Engineering and Management, Vol. 1, 2015, pp. 127-138