神经网络在网络入侵检测中的应用研究

Research on Intrusion Detection System Based on Neural Network

研究网络安全问题,针对网络受到非法用户入侵,破坏系统的正常工作,传统网络初始权值凭经验确定,易出现初始权值确定不当,导致网络入侵检测准确率低的难题。为了提高网络入侵检测的准确率,提出一种遗传神经网络的网络入侵检测方法。方法把神经网络和遗传算法结合起来,把网络初始权值作为遗传算法的一个种群,把网络检测准确率作为遗传算法的目标函数,通过遗传算法种群的"优胜劣汰"机制搜索到神经网络算法的全局最优初始权值,采用最优权值对网络入侵数据进行检测,得到最优网络入侵检测结果。结果证明,方法学习速度快、检测准确率高、漏报率与误报率低,克服传统网络检测方法不准确的缺陷。

The intrusion detection based on neural network is a common intrusion detection method of intelligence.Weights of standard neural network,are determined by experience,thus the weights may be determined improper ly and cause the neural network run into the local optimal,which results in the problem of low accuracy.In order to improve the accuracy of network intrusion detection,this paper puts forward a intrusion detection methods based on neural network and genetic algorithm network.In the learning process of neural network,using genetic algorithm to optimize neural network weights and then the forecasting model is established.The method is applied in the intrusion detection.simulation and the experiment results with KDD CUP99 are good.It studies fast with high accuracy of categorises rate,network,.