改进的HS算法优化BP神经网络的入侵检测研究

基于传统BP神经网络的入侵检测中，BP神经网络算法模型存在着易陷入局部最优且初始值随机性较大的缺陷。初始值的选择直接影响到BP神经网络的训练效果，较好的初始值有利于BP神经网络跳过局部最优，从而提高训练效率。针对BP神经网络的缺陷，提出了用改进的和声搜索算法对BP神经网络的初始值进行优化，使得BP神经网络得到一组较优的初值的方法。实验结果显示，改进的和声搜索算法具有更高的适应度函数值，将该算法优化的BP神经网络用在入侵检测中，能够显著提高算法检测率和收敛速率。

Optimizing intrusion detection of BP neural networks by a modified harmony search algorithm

In intrusion detection based on traditional BP networks, the model of the BP network algorithm is easy to fall into local optimum and the initial value is random. So how to choose initial value directly affects the training effect of BP networks. We therefore propose an improved harmony search (HS) algorithm to optimize the initial value of BP neural networks, which gets the BP neural network a set of better initial values. Experimental results show that the improved HS algorithm has higher fitness function value, and the detection rate and convergence rate of the algorithm are improved when the BP network optimized by this algorithm is used in intrusion detection.