Comparative Analysis of System Logs and Streaming Data Anomaly Detection Algorithms

Abstract

This paper provides with the description, comparative analysis of multiple commonly used approaches of the analysis of system logs, and streaming data massively generated by company IT infrastructure with an unattended anomaly detection feature. An importance of the anomaly detection is dictated by the growing costs of system downtime due to the events that would have been predicted based on the log entries with the abnormal data reported. Anomaly detection systems are built using standard workflow of the data collection, parsing, information extraction and detection steps. Most of the document is related to the anomaly detection step and algorithms like regression, decision tree, SVM, clustering, principal components analysis, invariants mining and hierarchical temporal memory model. Model-based anomaly algorithms and hierarchical temporary memory algorithms were used to process HDFS, BGL and NAB datasets with ~16m log messages and 365k data points of the streaming data. The data was manually labeled to enable the training of the models and accuracy calculation. According to the results, supervised anomaly detection systems achieve high precision but require significant training effort, while HTM-based algorithm shows the highest detection precision with zero training. Detection of the abnormal system behavior plays an important role in large-scale incident management systems. Timely detection allows IT administrators to quickly identify issues and resolve them immediately. This approach reduces the system downtime dramatically.Most of the IT systems generate logs with the detailed information of the operations. Therefore, the logs become an ideal data source of the anomaly detection solutions. The volume of the logs makes it impossible to analyze them manually and requires automated approaches.