Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses

Guo Zhou; Fahui Miao; Zhonghua Tang; Yongquan Zhou; Qifang Luo

doi:10.3389/fncom.2023.1079483

Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses

Front Comput Neurosci. 2023 Feb 22:17:1079483. doi: 10.3389/fncom.2023.1079483. eCollection 2023.

Authors

Guo Zhou¹, Fahui Miao², Zhonghua Tang^{2

3}, Yongquan Zhou^{2

3}, Qifang Luo^{1

2}

Affiliations

¹ Department of Science and Technology Teaching, China University of Political Science and Law, Beijing, China.
² College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
³ Guangxi Key Laboratories of Hybrid Computation and Integrated Circuit (IC) Design Analysis, Nanning, China.

Abstract

Introduction: The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.

Methods: In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.

Results: The clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network.

Discussion: Our approach was verified with the KDDCUP99 network intrusion data. The experimental results show that SOS-Kohonen can effectively detect intrusion. The detection rate was higher, and the false alarm rate was lower.

Keywords: Kohonen neural network; detection rate; false alarm rate; intrusion detection; symbiotic-organism search algorithm.

Grants and funding

This work was supported by National Natural Science Foundation of China under Grant Nos. U21A20464 and 62066005, and Program for Young Innovative Research Team in China University of Political Science and Law, under Grant No. 21CXTD02.