基于序列模式挖掘的预警关联算法

Y. Lv, Yuanlong Li, Shuang Xiang, C. Xia, Jingxin Geng
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引用次数: 4

摘要

序列关联方法在识别未知攻击时有局限性,需要预先定义攻击行为之间的因果关系。为了解决这一问题,本文提出了一种基于序列模式挖掘的预警关联算法TPrefixSpan, TPrefixSpan算法在PrefixSpan算法的基础上引入了可以彻底缩小搜索空间的时间间隔,从而大大节省了序列模式挖掘中重复数据集扫描的时间成本,保证了PrefixSpan算法的效率。与PrefixSpan算法相比,TPrefixSpan算法对攻击的识别精度更高。为了更好地将关联规则可视化,提出了一种攻击行为序列图生成算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An alert correlation algorithm based on the sequence pattern mining
Sequence correlation method has limits in unknown attacks identification and requires pre-defining the causal relationship between attack behavior. To solve this problem, an alert correlation algorithm, denoted as TPrefixSpan, based on the sequence pattern mining is proposed in this paper, based on PrefixSpan algorithm, TPrefixSpan algorithm introduces time interval that can thoroughly narrow, the search space, then time cost on repeated dataset scan in the sequence pattern mining is greatly saved, the efficiency of the PrefixSpan algorithm is ensured. Compared with PrefixSpan algorithm, TPrefixSpan algorithm generates much precise attacks identification. In order to visualize the correlation rules better, a sequence diagram generation algorithm of attack behavior is put forward.
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