当前的入侵检测技术主要有基于规则的误用检测和基于统计的异常检测。本文提出一个基于遗传算法的神经网络入侵检测系统模型,该模型将神经网络与遗传算法结合起来,利用神经网络自学习、自适应的特性,同时克服了神经网络易陷入局部最优,训练速度慢的缺点。该模型具有智能特性,能够较好地识别新的攻击。 Current intrusion detection techniques mainly include rule-based misuse detection and statistical anomaly detection. This paper presents a neural network intrusion detection system based on genetic algorithm model, the model combines neural network and genetic algorithm, the use of neural network self-learning, adaptive features, while overcoming the neural network easily fall into the local optimum, slow training Shortcomings. The model is intelligent and better able to identify new attacks.