应用新的机器学习策略提高入侵检测系统的性能

Tao Zou, Yimin Cui, Minhuan Huang, Cui Zhang
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引用次数: 4

摘要

误用检测方法最严重的问题是无法检测到新的攻击类型。为了解决这一问题,提出了一种更好的检测方法,该方法使用了一种新的学习策略。攻击标签的概念层次生成(CHGL)应用相关特征子集代码聚类,使常见的机器学习算法在高概念层次上学习攻击概况。这将使系统能够检测到更多的攻击实例。实验结果表明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving performance of intrusion detection system by applying a new machine learning strategy
The most acute problem for misuse detection method is its inability to detect new kinds of attacks. A better detection method, which uses a new learning strategy, is proposed to solve this problem. A Concept Hierarchy Generation for attack Labels (CHGL) applying relevant feature subset codes clustering, makes common machine learning algorithms learn attack profiles on high concept levels. And that will enable the system detect more attack instances. Experimental results show the advantage of this new method.
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