An Attention-Based GRU Network for Anomaly Detection from System Logs

Yixi XIE  Lixin JI  Xiaotao CHENG  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.8   pp.1916-1919
Publication Date: 2020/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDL8016
Type of Manuscript: LETTER
Category: Information Network
anomaly detection,  GRU,  attention-based model,  

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System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.