SiaLog: detecting anomalies in software execution logs using the siamese network

Detecting anomalies in software logs has become a notable concern for software engineers and maintainers as they represent anomalies in software execution paths and states. This paper propose a novel anomaly detection approach based on the Siamese network on top of Recurrent Neural Networks(RNN). Accordingly, we introduce a novel training pair generation algorithm to train the Siamese network which reduces generated training significantly while maintaining the F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} score. Additionally, we propose a hybrid model by combining the Siamese network with a traditional feedforward neural network to make end-to-end training possible, reducing engineering effort in setting up a deep-learning-based log anomaly detector. Furthermore, we provides validations of the approach on the Hadoop Distributed File System (HDFS), Blue Gene/L (BGL), and Hadoop map-reduce task log datasets. To the best of our knowledge, the proposed approach outperforms other methods on the same dataset at the F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} scores of respectively 0.99, 0.99, and 0.94 on HDFS, BGL, and Hadoop datasets, resulting in a new state-of-the-art performance.To further evaluate the proposed method, we examine our method’s robustness to log evolutions by evaluating the model on synthetically evolved log sequences; we got the F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} score of 0.95 on the HDFS dataset at the noise ratio of 20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document}. Finally, we dive deep into some of the side benefits of the Siamese network. Accordingly, we introduce an unsupervised log evolution monitoring method alongside a visualization technique that facilitates model interpretability.