【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。更多还原

【Abstract】 In the Internet,computers and equipment are threaded by malicious intrusion,and the safety of network is seriously affected.Intrusion behavior has features of upgraded fast,strong concealment,random characteristics,so the traditional 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 simulation results show the effectiveness of the algorithm.更多还原