基于MSNN模型的网络安全入侵检测

Intrusion Detection of Network Security Based on MSNN Model

为解决网络系统入侵行为升级快,隐蔽性强和随机性高等严重安全问题,结合入侵检测系统信息的特点,提出一种基于MSNN模型的入侵检测算法。首先提取系统调用顺序特性和频度特性,然后利用多级Sigmoid神经网络中的Sigmoid神经元具有微调网络的作用,且能让神经元产生多元反应进行多类分类,构建类似于大脑神经突触网络信息处理的MSNN模型,实现网络安全入侵检测。实验结果表明,该算法的检测精度高、抗干扰能力强,具有良好的检测效果和较高的应用价值。

In order to solve the serious security problems of network system intrusion behavior,such as rapid upgrade,strong concealment and high randomness,an intrusion detection algorithm based on MSNN model is proposed in combination with the characteristics of intrusion detection system information. First extract the system transfer sequence characteristics and frequency characteristics,then the algorithm use Sigmoid neurons in the multilevel Sigmoid neural network to fine-tune the network and enable the neurons to generate multiple responses for multiple classification,so as to build an MSNN model similar to the brain’s synaptic network information processing and realize network security intrusion detection. The experimental results show that the proposed algorithm has high precision and strong anti-interference ability,and has a good detection effect and high application.