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

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 ANIDS (Anomaly NIDS) based BPNN, called "ANIDS BPNN-GASAA" outperforms the original ANIDS BPNN, ANIDS 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.

[1]  Lee Jacobson,et al.  Genetic Algorithms in Java Basics , 2015, Apress.

[2]  Mohammad Javad Golkar,et al.  A hybrid method consisting of GA and SVM for intrusion detection system , 2016, Neural Computing and Applications.

[3]  D. Ashok Kumar,et al.  A Novel Algorithm for Network Anomaly Detection Using Adaptive Machine Learning , 2018 .

[4]  Kwangjo Kim,et al.  Another Fuzzy Anomaly Detection System Based on Ant Clustering Algorithm , 2017, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[5]  Samarjeet Borah,et al.  An Enhanced Intrusion Detection System Based on Clustering , 2018 .

[6]  Bhavin Shah,et al.  Artificial Neural Network based Intrusion Detection System: A Survey , 2012 .

[7]  Chen Chang,et al.  Application of Back Propagation Neural Network with Simulated Annealing Algorithm in Network Intrusion Detection Systems , 2017 .

[8]  Ruby Sharma,et al.  An Enhanced Approach to Fuzzy C-means Clustering for Anomaly Detection , 2018 .

[9]  Mohamed Rida,et al.  A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection , 2018, Comput. Secur..

[10]  Yang Yu,et al.  A Hybrid Methodologies for Intrusion Detection Based Deep Neural Network with Support Vector Machine and Clustering Technique , 2016 .

[11]  Mohamed Guerroumi,et al.  A New Classification Process for Network Anomaly Detection Based on Negative Selection Mechanism , 2016, SpaCCS Workshops.

[12]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[13]  Taufik Abrão,et al.  Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic , 2018, Expert Syst. Appl..

[14]  N. Lokeswari,et al.  Artificial Neural Network Classifier for Intrusion Detection System in Computer Network , 2016 .