Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection
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Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection
Issue 9, Pages: 47-52(2006)
作者机构:
国防科学技术大学电子科学与工程学院 2. 总参第61研究所
作者简介:
基金信息:
DOI:
CLC:TP393.08
Published:2006
稿件说明:
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MA Lin-ru1, YANG Lin2, WANG Jian-xin2, et al. Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection[J]. 2006, (9): 47-52.
DOI:
MA Lin-ru1, YANG Lin2, WANG Jian-xin2, et al. Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection[J]. 2006, (9): 47-52.DOI:
Using fuzzy clustering to reconstruct alert correlation graph of intrusion detection
摘要
众多的入侵检测告警关联方法中
因果关联是最具代表性的方法之一。针对因果关联在一些条件下会引发关联图分裂的问题
提出利用模糊聚类的方法实现攻击场景重构。在聚类过程中
针对告警特性提出一种基于属性层次树的相似度隶属函数定义方法
并给出评价相似度度量和衡量攻击场景构建能力的若干指标。实验结果表明
该方法能够有效地组合分裂的关联图
重构攻击场景。
Abstract
Causal correlation method was one of the most representative methods for instruction detection alert correla-tion. In some conditions
the correlation graph would be split because of loss of causal information. In order to solve the problem
an algorithm was proposed to reconstruct attack scenario using fuzzy clustering. A new similarity membership function based on the attribute hierarchy tree was defined in the process of clustering. Furthermore
the evaluation method and indexes were put forward to describe the ability of reconstructing attack scenario. The experimental results indicate that this algorithm is valid to combine the split correlation graph and reconstruct attack scenario.