基于日志的异常检测技术综述

doi:10.11959/j.issn.2096-109x.2020072

Abstract

Abstract:

Log information has become an important information resource in the rapid development of information systems.Through the analysis of logs,abnormal detection,fault diagnosis and performance diagnosis can be performed.The log-based anomaly detection technology was focused on.Firstly,the currently used log-based anomaly detection framework was introduced,and then the key link technologies such as log analysis and log anomaly detection were focused on.Finally,the current technology was summarized and suggestions for future research directions were given.

Key words: abnormal detection, log analysis, machine learning

CLC Number:

TP393

Yingjun ZHANG,Ushangqi LI,Mu YANG,Haixia ZHANG,Kezhen HUANG. Survey on anomaly detection technology based on logs[J]. Chinese Journal of Network and Information Security, 2020, 6(6): 1-12.

Figures/Tables 5

References 60

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Eid	Name	Message	Frequency	Parameters
1	server.ZooKeeperServer	Server environment	2	host.name=localhost,user.home=/home/hadoop
2	server.NIOServerCnxnFactory	binding to port	1	0.0.0.0/0.0.0.0:2181
3	server.NIOServerCnxnFactory	Accepted socket connection	1	192.168.31.154:38221
4	server.ZooKeeperServer	Client attempting to establish new	1	192.168.31.154:38221
		session
5	server.ZooKeeperServer	Established session	1	0x1621970549a0000
	…	…	…	…

名称	类别	使用算法	模式	准确性	时间复杂度	效率
CFG^[25]	代码分析	AST,CFG	离线	++	O(n³)	+++
PCA^[26]	代码分析	AST	在线	+++	O(n)	+++
CLSTR^[29]	机器学习	IPLoM	离线	++	O(n)	+++
LKE^[31]	机器学习	Clustering	离线	++	O(n²)	+
Logram^[36]	自然语言处理	n-gram	在线	+++	O(n)	+++
NLog^[39]	自然语言处理	POS	离线	++	O(n)	+++
Spell^[22]	经典算法	LCS	在线	+++	O(n)	+++
Drain^[43]	经典算法	解析树	在线	+++	O(n)	+++

名称	分类	使用的算法	模式	准确率	召回率
AClog^[54]	监督学习	SVM,LCS	离线	92.4%	80%
IM^[56]	监督学习	K-prototype,kNN	离线	89%	85%
LogClass^[55]	监督学习	PU,SVM	在线	99.048%	99.988%
LogCluster^[59]	无监督学习	聚类算法	在线	60%	36.2%
MCL^[23]	无监督学习	PCA	在线	99.8%	/
LACT^[63]	无监督学习	TCA,NLP	离线	97.08%	95.45%
CausalConvLSTM^[73]	深度学习	CNN,LSTM	离线	89.59%	99.72%
DeepLog^[74]	深度学习	LSTM	在线	95%	96%
LogGAN^[75]	深度学习	LSTM	离线	100%	35.6%
LogRobust^[76]	深度学习	Bi-LSTM	在线	98%	100%

Survey on anomaly detection technology based on logs

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