In Frontiers in computational neuroscience
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.
Zhou Guo, Miao Fahui, Tang Zhonghua, Zhou Yongquan, Luo Qifang
2023
Kohonen neural network, detection rate, false alarm rate, intrusion detection, symbiotic-organism search algorithm
