Comparison study of evolutionary flexible neural networks for Intrusion Detection System

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 flexible neural networks and flexible neural tree trained by recent various evolutionary algorithms, i.e., Estimation of Distribution Algorithm (EDA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for detecting intrusions in a network. Results are then compared with Decision Trees (DT) classifier. Empirical results clearly show that evolutionary computing and neural networks techniques could play an important role in designing real time intrusion detection systems.