Anomaly Detection and Root Cause Analysis on Log Data

被引：0

作者：

Pasha, Daem ^{[1
]}

Shah, Ali Hussain ^{[1
]}

Zadeh, Esmaeil Habib ^{[1
]}

Konur, Savas ^{[1
]}

机构：

[1] Univ Bradford, Dept Comp Sci, Bradford BD7 1DP, W Yorkshire, England

来源：

ARTIFICIAL INTELLIGENCE XXXIX, AI 2022 | 2022年 / 13652卷

基金：

英国工程与自然科学研究理事会; “创新英国”项目;

关键词：

Root cause analysis; Anomaly detection; Log files; Machine learning;

D O I：

10.1007/978-3-031-21441-7_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we perform anomaly detection and root cause analysis on log data (system logs). Firstly, we employ a log parsing solution known as Drain (an online log parsing approach with fixed depth tree). We then present an anomaly detection approach that utilizes a decision tree model. This will be used to determine the anomalous devices in the log files. One benefit of decision tree models is that they are easily traceable, providing a contrast to most "black-box" solutions currently available in the industry. Finally, a sequential model using Keras is built to predict the root cause of a given issue.

引用

页码：333 / 339

页数：7

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