Zhou Guo, Miao Fahui, Tang Zhonghua, Zhou Yongquan, Luo Qifang
Department of Science and Technology Teaching, China University of Political Science and Law, Beijing, China.
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
Front Comput Neurosci. 2023 Feb 22;17:1079483. doi: 10.3389/fncom.2023.1079483. eCollection 2023.
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.
In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.
The clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network.
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.
互联网的发展使生活更加便捷，但网络入侵形式日益多样化，对网络安全的威胁也日益严重。因此，入侵检测研究对网络安全变得非常重要。
本文提出了一种基于共生生物搜索（SOS）算法和Kohonen神经网络的聚类算法。
通过使用SOS算法优化Kohonen神经网络中的权重，提高了Kohonen神经网络的聚类精度。
我们的方法通过KDDCUP99网络入侵数据进行了验证。实验结果表明，SOS-Kohonen能够有效地检测入侵。检测率更高，误报率更低。
