蚁群算法选择神经网络参数的网络入侵检测  

Network intrusion detection based on ant colony optimization algorithm selecting parameters of neural network

作　　者：刘芳芳 

机构地区：[1]长春理工大学光电信息学院信息工程分院,吉林长春130000

出　　处：《现代电子技术》2017年第21期80-83,共4页Modern Electronics Technique

摘　　要：为了解决网络入侵检测率低的难题,提出蚁群算法选择神经网络参数的网络入侵检测模型(ACO-NN)。首先收集网络入侵检测数据,然后采用神经网络对入侵检测数据进行学习,通过蚁群算法解决神经网络参数选择问题,最后采用标准入侵检测数据进行验证性测试,并与其他模型进行对比分析。结果表明,所提模型解决了神经网络参数优化难题,降低了网络入侵检测的错误率,改善了网络入侵检测的正确率,有助于保证网络的安全性。In order to improve the precision of network intrusion detection,a network intrusion detection model based on ant colony optimization selecting parameters of neural network is proposed. The data of network intrusion detection is collected,and the neural network is used to learn the intrusion detection data. The ant colony optimization algorithm is employed to select the parameters of neural network,which is verified with the standard intrusion detection data. The contrastive analysis is performed for the intrusion detection model and other models. The results show that the model can solve the difficulty of neural network parameter optimization,reduce the error rate of network intrusion detection,improve the precision of network intrusion detection,and is conducive to ensuring the network security.

关 键 词：网络安全 非法用户 入侵检测 蚁群算法 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP391[自动化与计算机技术—计算机科学与技术]