关键词: 支持向量机, 软间隔, 入侵检测, 1类支持向量机, 无监督学习

Abstract: The soft-margin SVM is one of the best machine learning algorithms to classify abnormal behaviors in intrusion detection.Because it is supervised learning methods，it is not appropriate to use the soft-margin SVM method for detecting novel attacks in Internet traffic.One-class SVM approach is an unsupervised learning method for detecting anomalies.But it is difficult to use the one-class SVM in the real world，due to its high false positive rate.In this paper，a new SVM approach is proposed which combines these two methods in order to provide unsupervised learning and low false alarm capability，similar to that of the soft-margin SVM approach.

Key words: Support Vector Machine, intrusion detection, soft-margin, one-class Support Vector Machine, unsupervised learning