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

针对入侵检测系统的自主学习性、实时性,提出带变异算子的粒子群优化方法,并用该方法优化BP神经网络以加快其收敛速度,提出了MPSO_BP混合优化算法.为提高入侵检测系统的检测率、降低误报率,提出了一种新的入侵检测模型(MPBIDS).采取Iris数据集对3个BP神经网络进行模拟实验,结果表明,优化后的BP神经网络具有更好的收敛速度和精度.将改进的BP神经网络应用到入侵检测中,采取KDDCUP99为测试数据集,仿真结果表明,基于改进BP神经网络的入侵检测模型能提高检测率、降低误报率.

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 better convergence speed and accuracy. Based on this finding, the improved BP network was applied to intrusion detection, taking KDDCUP99 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.