基于支持向量机的入侵检测研究

Liang Bo, C. Yuan
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引用次数: 1

摘要

入侵检测在网络安全中占有重要的地位,因此得到了迅速的发展。将基于统计学习理论的支持向量机方法应用于入侵检测系统中,有效地对检测数据进行分类,达到了支持向量机能够准确预测系统异常状态的目的。该方法避免了传统机器学习方法的局限性,保证了更强的扩展能力,使入侵检测系统具有更好的检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Research of Intrusion Detection Based on Support Vector Machine
Intrusion detection is developed quickly because which has important position in network security. The method of SVM based on statistics learning theory is used in the intrusion detection system, which classifies detecting data efficiently, and achieves the aim that SVM can accurately predict the abnormal state of system. By the use of this method, the limitation of traditional machine learning method is avoided and ensures the stronger extension ability which makes intrusion detection system to have the better detecting performance.
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