Abstract:
Network intrusion detection data are made up of multi-class attacks data and normal data. The application of multi-class support vector machine(SVM) for network intrusion detection is researched. The multi-class support vector machine is designed to detect network intrusion by using one-against-one method. The designed SVM classifier is evaluated with KDD99 intrusion detection dataset, the results obtained are compared with BP neural networks. Experimental results show that classifier based on multi-class SVM is effective and efficient.
Key words:
intrusion detection,
statistical learning theory(SLT),
multi-class SVM,
kernel function
摘要: 网络入侵检测所处理的数据由多类攻击数据和正常数据构成,基于此对多分类支持向量机在网络入侵检测中的应用进行了研究,采用一对一方法构造了多分类支持向量机分类器,用KDD99入侵检测数据对所提出的多分类支持向量机分类器进行了测试评估,将实验结果和BP神经网络方法进行了比较。实验表明提出的方法是可行的、高效的。
关键词:
入侵检测,
统计学习理论,
多分类支持向量机,
核函数
CLC Number:
YI Zhi-an; LV Man. Intrusion Detection Method Based on Multi-class Support Vector Machines[J]. Computer Engineering, 2007, 33(15): 167-169.
衣治安;吕 曼. 基于多分类支持向量机的入侵检测方法[J]. 计算机工程, 2007, 33(15): 167-169.