Novel Framework Based on Genetic Algorithm and Simulated Annealing Algorithm for Optimization of BP Neural Network Applied to Network IDS

Abstract

Nowadays, network security is a world hot topic in computer security and defense. Intrusions, attacks or anomalies in network infrastructures lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is an effective countermeasure and high-profile method to detect the unauthorized use of computer network and to provide the security for information. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Experimental results on KDD CUP' 99 dataset show that our optimized (Anomaly NIDS) based BPNN, called ANIDS BPNN-GASAA outperforms the original BPNN, BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate and false positive rate, and it is very much suitable for network anomaly detection.