Collaborative quantum optimization network intrusion detection research

Article type: Research Article

Authors: Zeng, Shuhua^a | Huang, Yinxiu^{b; *}

Affiliations: [a] Hunan Vocational College of Railway Technology, Zhuzhou 412006, Hunan, China | [b] Hunan Chemical Vocational Technology College, Zhuzhou 412004, Hunan, China

Correspondence: [*] Corresponding author: Yinxiu Huang, Hunan Chemical Vocational Technology College, Zhuzhou 412004, Hunan, China. E-mail: 16476307@qq.com.

Abstract: In order to improve network intrusion detection rate, a cooperative quantum PSO and LS-SVM network intrusion detection model (CQPSO-LSSVM) was proposed in this paper. Network feature subset is encoded into quantum particle positions, intrusion detection accuracy is used as the evaluation criteria of a subset feature merits, a synergistic quantum particle swarm algorithm are used to find the optimal feature subset, LS-SVM is used to establish a network intrusion detection model, and KDD CUP 99 dataset is used to simulation test. The results show that, compared with other models, the proposed algorithm has improved detection efficiency and the detection rate of the network intrusion.

Keywords: Cooperative quantum-behaved particle swarm optimization algorithm, least square support vector machine, feature selection, network intrusion detection

DOI: 10.3233/JCM-180875

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 19, no. 1, pp. 169-178, 2019