| 改进的基于日志聚类的异常检测方法 |
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| 引用本文： | 冯士龙,台宪青,马治杰. 改进的基于日志聚类的异常检测方法[J]. 计算机工程与设计, 2020, 41(4): 1087-1092 |
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| 作者姓名： | 冯士龙 台宪青 马治杰 |
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| 作者单位： | 中国科学院物联网研究发展中心数据与服务研发中心,江苏无锡214135;中国科学院大学微电子学院,北京101407;中国科学院电子学研究所苏州研究院地理空间信息系统研究室,江苏苏州215121;中国科学院物联网研究发展中心数据与服务研发中心,江苏无锡214135 |
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| 基金项目： | 中国科学院战略性先导科技专项 |
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| 摘 要： | 针对基于日志聚类的异常检测方法(LogCluster)处理的日志类型单一的问题,提出一种改进的基于LogCluster的日志异常检测方法,SW-LogCluster。通过使用滑动窗口(sliding window)的方式将日志划分为日志序列,将划分后的日志序列向量化来进行特征提取,使其既能检测带标记符的日志,也能检测不带标记符的日志,扩展原始方法的应用范围。实验结果表明,SW-LogCluster方法能对所有类型的非结构化日志进行检测,有效扩展了LogCluster方法的适用性。
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| 关 键 词： | 异常检测 滑动窗口 日志序列划分 日志聚类 日志向量化 |
Improved log clustering-based anomaly detection method |
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| FENG Shi-long,TAI Xian-qing,MA Zhi-jie. Improved log clustering-based anomaly detection method[J]. Computer Engineering and Design, 2020, 41(4): 1087-1092 |
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| Authors: | FENG Shi-long TAI Xian-qing MA Zhi-jie |
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| Affiliation: | (Research and Development Center of Data and Service,Research and Development Center for Internet of Things,Chinese Academy of Sciences,Wuxi 214135,China;School of Microelectronics,University of Chinese Academy of Sciences,Beijing 101407,China;Laboratory of Geospatial Information Systems,Institute of Electronics,Chinese Academy of Sciences,Suzhou 215121,China) |
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| Abstract: | Regarding the problem that the log type resolved using the log clustering-based anomaly detection(LogCluster)is too simple,an improved LogCluster-based log anomaly detection method,SW-LogCluster,was proposed.Logs were divided into log sequences using a sliding window,and the divided log sequences were vectorized for feature extraction,so that both the labled log and the non-labled log were detected,which extended the scope of application of the original method.Experimental results show that the SW-LogCluster method can detect all types of unstructured logs,which effectively extends the applicability of the LogCluster method. |
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| Keywords: | anomaly detection sliding window log sequence division log clustering log vectorization |
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