Substructure-aware Log Anomaly Detection

Yanni Tang

doi:10.14778/3705829.3705840

2025-02-28

PRE

DOI：10.14778/3705829.3705840

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摘要

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系统日志记录了关于系统操作的关键信息，是系统异常检测不可或缺的工具。基于图的方法在捕捉日志事件间的相互依赖关系方面表现出优于其他方法的性能。然而，现有方法往往忽略了日志图节点内部的复杂子结构模式，导致难以捕捉指示节点异常的事件类型、结构及异常位置的细微变化。为解决此局限性，本文提出了一种新颖的框架，称为代码文件级子结构感知日志异常检测（SLAD）。它首先引入了一种专门针对日志异常检测的蒙特卡洛树搜索策略，以发现代表性子结构。然后，SLAD采用子结构蒸馏方法，基于代表性子结构来提升异常推理的效率。接着，我们引入软剪枝技术以获取节点的关键子结构。实验结果表明，SLAD优于所有基线方法。特别是，SLAD在异常推理方面的速度比基于子结构的图学习方法快至少15倍。

期刊

Proceedings of the VLDB Endowment

IF:

3.3

论文数:

563

被引数:

1.2W

机构

Univ Auckland

学者数:

853

论文数: 604

被引数: 194

Southeast Univ

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5.4K

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被引数: 836

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