一种融合变量的日志异常检测方法

doi:10.3969/j.issn.1671-1122.2023.10.003

信息网络安全 ›› 2023, Vol. 23 ›› Issue (10): 16-20.doi: 10.3969/j.issn.1671-1122.2023.10.003

一种融合变量的日志异常检测方法

张玉臣, 李亮辉, 马辰阳, 周洪伟()

中国人民解放军信息工程大学密码工程学院，郑州 450001

收稿日期:2023-06-04 出版日期:2023-10-10 发布日期:2023-10-11
通讯作者: 周洪伟 E-mail:hong_wei_zhou@126.com
作者简介:张玉臣（1977—），男，河南，教授，博士，主要研究方向为保密管理|李亮辉（2000—），男，河北，本科，主要研究方向为保密管理|马辰阳（2000—），男，河南，本科，主要研究方向为网络信息防御|周洪伟（1979—），男，重庆，副教授，博士，主要研究方向为网络信息安全
基金资助:
国家自然科学基金(61902427)

A Log Anomaly Detection Method with Variables

ZHANG Yuchen, LI Lianghui, MA Chenyang, ZHOU Hongwei()

Department of Cryptographic Engineering, Information Engineering University of PLA, Zhengzhou 450001, China

Received:2023-06-04 Online:2023-10-10 Published:2023-10-11

摘要/Abstract

摘要：

为了充分挖掘日志中变量的潜能，优化日志异常检测效果，文章提出一种融合变量的日志异常检测方法SiEv。首先，该方法可以识别主体变量，并根据主体变量将日志划分为不同片段；然后，SiEv以这些日志片段为输入，基于长短期记忆网络（Long Short-Term Memory，LSTM）训练或检测异常，从而避免不同主体的日志序列特征相互干扰；最后，根据日志片段将SiEv划分为多个类别，从不同角度检测日志。为了验证文章所提方法的有效性，SiEv对Loghub所提供的日志数据集进行测试。实验结果表明，SiEv能够发现多种类型日志中存在的异常，识别同一主体的活动行为模式和变化趋势。

关键词: 日志, 异常检测, LSTM, 变量

Abstract:

In order to fully tap the potential of variables in logs and optimize the effectiveness of log anomaly detection, this paper proposed a novel log anomaly detection method SiEv with the variables. Firstly, this method identified the subject variable in the log, and divided the log into different fragments based on the subject variable. Then, SiEv took these fragments as input for LSTM to avoid mutual interference between log sequence features of different subjects. Finally, according to different log fragments, SiEv was able to be divided into multiple categories to detect logs with the view of different perspectives. To verify the effectiveness of the method, SiEv was tested with the log dataset provided by the Loghub. The experimental results indicate that SiEv is able to detect anomalies in various types of logs, identify the activity behavior patterns and trends of the same subject.

Key words: log, anomaly detection, LSTM, variables

中图分类号:

TP309

张玉臣, 李亮辉, 马辰阳, 周洪伟. 一种融合变量的日志异常检测方法[J]. 信息网络安全, 2023, 23(10): 16-20.

ZHANG Yuchen, LI Lianghui, MA Chenyang, ZHOU Hongwei. A Log Anomaly Detection Method with Variables[J]. Netinfo Security, 2023, 23(10): 16-20.

图/表 4

参考文献 21

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日志类型	主体变量位置规律
Hadoop	<yyyy-MM-dd HH:mm:ss, SSS>\|<Log Level>\|<主体变量>\|<log中的message>
HDFS	<yyMMdd HHmmss>\|<Pid>\|<Log Level>\|<主体变量>\|<log中的message>
BGL	<Lable>\|<nnnnnnnnnn>\|<yyyy.MM.dd>\|<Node><Time>\|<NodeRepeat>\|<Type>\| <主体变量>\|<Log level>\|<log中的message>
OpenStack	<Logrecord>\|< yyyy-MM-dd HH:mm:ss, SSS>\|<Pid>\|<Log Level>\|<主体变量>\| <ADDR>\|< log中的message>

一种融合变量的日志异常检测方法

A Log Anomaly Detection Method with Variables

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