Evolutionary flexible neural networks for intrusion detection system

Authors:
Yuehui Chen;Lei Zhang;Ajith Abraham
Affiliations:
School of Information Science and Engineering, Jinan University, Jinan, P.R. China;School of Information Science and Engineering, Jinan University, Jinan, P.R. China;School of Computer Science Chung-Ang University, Seoul, Republic Korea
Venue:
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Year:
2006

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Abstract

An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. This paper evaluates the performances of Estimation of Distribution Algorithm (EDA) to train a feedforward neural network classifier for detecting intrusions in a network. Results are then compared with Particle Swarm Optimization (PSO) based neural classifier and Decision Trees (DT). Empirical results clearly show that evolutionary computing techniques could play an important role in designing real time intrusion detection systems.