基于人工鱼群算法优化神经网络在网络入侵检测中的应用研究

摘要

为解决传统入侵检测算法存在的检测正确率低、高误播率和检测效率低的问题，结合BP神经网络算法在网络入侵检测中的优点，提出一种采用人工鱼群算法优化BP神经网络算法的方法。通过仿真实验表明，采用优化的神经网络对入侵数据进行学习和检测，与传统网络入侵检测算法相比，具有较高的检测准确率和效率，可以很好地检测各种网络入侵类型，大大提高了网络的安全性能。%In order to solve the problems of low detection accuracy,high false alarm rate and low detection efficiency exis?ting in the traditional intrusion detection algorithm,a method of using artificial fish swarm algorithm to optimize the BP neural network algorithm is proposed in combination with the advantages of BP neural network algorithm in network intrusion detection. The simulation experiment results show that,in comparison with the traditional intrusion detection algorithm,the optimized neu?ral network has higher accuracy and efficiency while learning and detecting the intrusion data,can detect various network intru?sion types better,and improve the network safety performance greatly.