基于因素神经网络的入侵检测模型

Intrusion Detection Model Based on Factor Neural Networks

通过对网络攻击和防御的分析,提出一种基于因素神经网络理论(FNN)的入侵检测模型,描述入侵检测模型的结构和工作流程,将解析型因素神经网络和模拟型因素神经网络结合起来,解决对复杂入侵行为建模难的问题。通过实验对模型进行验证,实验表明该模型对已知入侵行为检测的准确度高,对未知入侵行为也能做出准确的判断。

Through the analysis of network attack and defense, a model of intrusion detection based on the theory of factor neural networks （FNN） is proposed. The structure and the working process of intrusion detection model are described. Combined with the analogous factor neural networks, the analytic factor neural networks can solve the problem of modeling for complex intrusion behavior. Finally an experimental verification of the model is given, which proves that the model of network intrusion detection with high accuracy can accurately judge the known and unknown intrusion behavior.