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 chapter, 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 (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP’99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-IGASAA” outperforms the original ANIDS BPNN, ANIDS BPNN optimized by using only GA and several traditional and new techniques in terms of detection rate, false positive rate and it is very much appropriate for network anomaly detection.
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Chiba, Z., Abghour, N., Moussaid, K., El omri, A., Rida, M. (2019). A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network. In: Ben Ahmed, M., Boudhir, A., Younes, A. (eds) Innovations in Smart Cities Applications Edition 2. SCA 2018. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-11196-0_43
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DOI: https://doi.org/10.1007/978-3-030-11196-0_43
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