蚁群算法选择神经网络参数的网络入侵检测  

Network intrusion detection based on neural network parameters selected by ant colony algorithm

作　　者：苏明[1] 马琳 SU Ming;MA Lin(Beijing Open University,Beijing 100018,China)

机构地区：[1]北京开放大学,北京100081

出　　处：《现代电子技术》2020年第22期114-117,共4页Modern Electronics Technique

基　　金：北京市自然科学基金重点项目(KZ201951160050)。

摘　　要：面对日益严峻的网络入侵形势,网络检测是保证网络安全的重要手段,因此提出蚁群算法选择神经网络参数的网络入侵检测方法。通过神经网络学习采集的网络入侵检测数据,学习过程中采用蚁群算法通过路径寻优、更新信息素等方式选择最佳的神经网络权值和阈值,得到最佳网络入侵检测模型,实现网络入侵的有效检测。实验结果表明,该方法具有较高的网络入侵检测准确率,检测网络入侵的效果更好,速度更快,且抗噪性能强;并且使用者对该方法的检测速度、错误率等方面均要优于传统方法,说明该检测方法的应用效果好、价值高。Network detection is an important mean to counter the increasingly severe network intrusion situation and ensure network security.Therefore,a network intrusion detection method based on neural network parameters selected by ant colony algorithm is proposed,which collects the network intrusion detection data by means of the neural network learning.In the process of learning,ant colony algorithm is used to select the best neural network weight and threshold by means of the path optimization,update pheromone and other ways to get the best network intrusion detection model and realize the effective detection of network intrusion.The experimental results show this method has higher accuracy of network intrusion detection,better effect of network intrusion detection,faster speed,and stronger anti-noise performance.The detection speed,error rate and other aspects of the method are better than those of the traditional method,which shows that the application effect and value of this detection method is excellent.

关 键 词：网络入侵检测 蚁群算法 神经网络 参数选择 数据采集 入侵检测模型 结果分析 

分 类 号：TN926-34[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]