面向网络入侵检测的反向综合学习粒子群优化算法研究

Research on Opposite Comprehensive Learning Particle Swarm Optimization for Network Intrusion Detection

摘要: 针对网络系统容易受到外部攻击的问题，本文提出了一种基于反向学习机制的异常网络行为入侵检测算法。论文首先提出了改进的粒子群算法，对支持向量机的参数进行优化，进而建立了网络入侵检测模型。实验结果验证了该方法可以提高分类器参数寻优的精度，增加了网络入侵行为识别的准确率。

Abstract: Aiming at the vulnerability of network systems to external attacks, this paper proposes an anomalous network behavior intrusion detection algorithm based on opposition-based learning mechanism. Firstly, an improved particle swarm optimization algorithm is proposed to optimize the parameters of support vector machine. Then a network intrusion detection model is provided. Experiments show that this method can improve the accuracy of classifier parameter optimization, and increase the accuracy of network intrusion identification.