The intrusion detection is a key technology to ensure the network security. In order to solve the problem of pa-rameter optimization of neural network in the application of intrusion detection,a network intrusion detection model based on ant colony algorithm selecting neural network parameter is proposed. The relation between the ant colony algorithm and neural network parameter is described. The objective function chosen by neural network parameters was established. The ant colony al-gorithm is used to search the optimal solution of objective function,and determine the optimal parameters of the neural network. The neural network self-organization learning is adopted to construct the classifier of intrusion detection. The simulation experi-ment for selected intrusion detection standard data was carried out on Matlab 2014 platform. The results show that the model can solve the problem of parameter optimization of neural network in the application of intrusion detection. The intrusion detection classifier with perfect comprehensive performance was established,and its classification result and classification rate are superior to the typical model.%入侵检测是保证网络安全的关键技术,为了解决神经网络在入侵检测应用中的参数优化难题,提出蚁群算法选择神经网络参数的网络入侵检测模型.首先描述蚁群算法与神经网络参数之间的联系,并建立神经网络参数选择的目标函数,然后采用蚁群算法对目标函数的最优解进行搜索,确定神经网络的最佳参数,最后通过神经网络自组织学习实现入侵检测分类器的构建,选择入侵检测标准数据在Matlab 2014平台上实现仿真实验.结果表明,该模型解决了神经网络在入侵检测中的参数优化难题,建立了综合性能良好的入侵检测分类器,分类结果和分类速度均比典型模型有较显著的优势.
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