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DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning Session F2: Insights from Log(in)s CCS ™17, October 30-November 3, 2017, Dallas, TX, USA DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar School of Computing, University of Utah {mind, lifeifei, guineng, svivek}@cs.utah.edu challenging and many traditional anomaly detection methods based on standard mining methodologies are no longer effective. System logs record system states and significant events at various critical points to help debug performance issues and failures, and perform root cause analysis. Such log data is universally available in nearly all computer systems and is a valuable resource for understanding system status. Furthermore, since system logs record noteworthy events as they occur from actively running processes, they are an excellent source of information for online monitoring and anomaly detection. Existing approaches that leverage system log data for anomaly detection can be broadly classified into three groups: PCA based approaches over log message counters [39], invariant mining based methods to capture co-occurrence patterns between different log keys [21], and workflow based methods to identify execution anomalies in program logic flows [42]. Even though they are successful in certain scenarios, none of them is effective as a universal anomaly http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

Du, Min; Li, Feifei; Zheng, Guineng; Srikumar, Vivek
Association for Computing Machinery — Oct 30, 2017
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Datasource
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISBN
978-1-4503-4946-8
doi
10.1145/3133956.3134015
Publisher site
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Abstract

Session F2: Insights from Log(in)s CCS ™17, October 30-November 3, 2017, Dallas, TX, USA DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar School of Computing, University of Utah {mind, lifeifei, guineng, svivek}@cs.utah.edu challenging and many traditional anomaly detection methods based on standard mining methodologies are no longer effective. System logs record system states and significant events at various critical points to help debug performance issues and failures, and perform root cause analysis. Such log data is universally available in nearly all computer systems and is a valuable resource for understanding system status. Furthermore, since system logs record noteworthy events as they occur from actively running processes, they are an excellent source of information for online monitoring and anomaly detection. Existing approaches that leverage system log data for anomaly detection can be broadly classified into three groups: PCA based approaches over log message counters [39], invariant mining based methods to capture co-occurrence patterns between different log keys [21], and workflow based methods to identify execution anomalies in program logic flows [42]. Even though they are successful in certain scenarios, none of them is effective as a universal anomaly

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