Title :
On the Efficiency of Support Vector Classifiers for intrusion detection
Author :
Ma, Zhenying ; Zhen, Lei ; Liao, Xiaofeng
Author_Institution :
Dept. of Comput. Sci. & Eng., Chongqing Univ.
Abstract :
We implement multi-class SVMs (by one-versus-rest, one-versus-rest method and a new decision tree (DT) SVM) for intrusion detection. None of these methods show advantages over two-class method wherever in detection accuracy or time cost in our experiments. We also apply a support vector (SV) reduction algorithm and find that it decreases the training time dramatically while improves the detection rate
Keywords :
decision trees; security of data; support vector machines; SVM; decision tree; intrusion detection; support vector classifiers; support vector machine; Artificial neural networks; Computer science; Costs; Decision trees; Intrusion detection; Kernel; Machine learning; Machine learning algorithms; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614773
