智能系统学报Issue(6)：558-563,6.DOI:10.3969/j.issn.1673-4785.201304040
变异粒子群优化的 BP 神经网络在入侵检测中的应用
Application of mutation particle swarm optimization based BP neural network in the intrusion detection system
摘要
Abstract
A aiming at the properties of real-time performance and self-learning of the intrusion detection system (IDS), an improved particle swarm optimization (PSO) based on the mutation operator was proposed , which was used to optimize BP neural network , so as to accelerate convergence speed of BP neural network , thus, the MPSO_BP hybrid optimization algorithm is presented .In order to increase detection rate and lower false alarm rate of the intrusion detection system , a new intrusion detection model ( MPBIDS) was put forward .Iris data set was applied to the three BP neural networks for simulation .Experiment results show that the optimized BP neural network had bet-ter convergence speed and accuracy .Based on this finding , the improved BP network was applied to intrusion de-tection , taking KDDCUP 99 as the test data set .The simulation result proves that the IDS with improved BP network can improve the detection rate and reduce the false alarm rate .关键词
变异算子/入侵检测系统/粒子群优化算法/BP神经网络Key words
mutation operator/intrusion detection system/particle swarm optimization/BP neural network分类
信息技术与安全科学引用本文复制引用
宋玲,常磊..变异粒子群优化的 BP 神经网络在入侵检测中的应用[J].智能系统学报,2013,(6):558-563,6.基金项目
国家自然科学基金资助项目（60963022）． （）
