基于SVM的网络入侵检测集成学习算法  

Network Intrusion Intelligent Detection Algorithm Based on AdaBoost

作　　者：谭爱平[1] 陈浩[1] 吴伯桥[1] 

机构地区：[1]湖南大学信息科学与工程学院,长沙410082

出　　处：《计算机科学》2014年第2期197-200,共4页Computer Science

基　　金：国家自然科学基金项目(61272190)资助

摘　　要：互联网络中,计算机和设备随时受到恶意入侵的威胁,严重影响了网络的安全性。入侵行为升级快、隐蔽性强、随机性高,传统方法难以有效防范。针对这一问题,提出一种基于SVM的网络入侵检测集成学习算法,该算法利用SVM建立入侵检测基学习器,采用AdaBoost集成学习方法对基学习器迭代训练,生成最终的入侵检测模型,仿真实验表明了该算法的有效性。In the Internet, computers and equipment are threaded by malicious intrusion, and the safety of network is se- riously affected. Intrusion behavior has features of upgraded fast, strong concealment, random characteristics, so the tra- ditional methods are difficult to prevent this problem effectively. In this paper, a network intrusion intelligent detection algorithm based on AdaBoost was presented. The SVM is used to build the learning-module of intrusion detection. The AdaBoost is used for training these learning-modules, and generating the final the intrusion detection model. The simula- tion results show the effectiveness of the algorithm.

关 键 词：安全 集成学习 入侵检测 ADABOOST SVM 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]