摘要
支持向量机(supportvectormachines)是一种建立在统计学习理论基础之上的机器学习方法。基于支持向量机在处理小样本、高维数及泛化能力强等方面的优势,该文提出了一种根据结构风险最小化原则基于支持向量机的入侵检测系统,首先简单介绍了入侵检测系统近来的发展状况和支持向量机的分类算法,然后给出以支持向量机分类算法为基础的入侵检测模型,以系统调用执行迹进行仿真实验,详细讨论了该模型的工作过程及核函数参数的选取对检测性能的影响。实验表明,该模型在先验知识较小的情况下,能够较好的检测出异常的入侵调用。
<Abstrcat>Support vector machine is a method of machine learning based on theory of statistics.The framework model proposed in this paper is a intrusion detection system based on support vector machine(SVM).First,the research process of intrusion detection and algorithm of SVM taxonomy are introduced.Then the model of an intrusion detection based on SVM is presented.System call trace data is used to emulate an intrusion detection experiment.The work process of this model is discussed and the choice of parameter of Kernel function is given to illustrate the performance of this model.The result of experiment shows that it can detect the abnormal intrusion under less prior knowledge.
出处
《计算机仿真》
CSCD
2005年第5期43-45,55,共4页
Computer Simulation
关键词
入侵检测
支持向量机
分类器
核函数
Intrusion detection
Support vector machine
Classifer
Kernel function
