改进遗传算法优化的BP神经网络入侵检测研究

Improved genetic algorithm to optimize BP neural network for intrusion detection

入侵检测是一种主动的安全防护技术,能够对网络内部和外部的攻击进行防御。基于神经网络的入侵检测是常用的智能检测方法,其中BP神经网络是比较常用的神经网络模型。针对BP神经网络算法易陷入局部极值和收敛速度慢等问题,将神经网络与遗传算法相结合,用改进的遗传算法优化BP神经网络权值。

Intrusion detection is a proactive security technology to defend the attack from internal and external of the network.Intrusion detection based on neural network is commonly used intelligence test method,in which BP neural network is commonly used neural network model.In this paper,BP neural network algorithm easily trapped into local minima and slow convergence problems,the neural network and genetic algorithm combining genetic algorithm with the improved BP neural network weights.Actual results show that the method can effectively improve the training accuracy and the detection rate,shorten the training time.