Sprelog - Log-Based Anomaly Detection with Self-matching Networks and Pre-trained Models.

With the development of software systems, log has become more and more important in system maintenance. During the past few years, log-based anomaly detection has attracted much attention. We propose a novel log-based anomaly detection model, called Sprelog, which captures "inconsistent" information during the evolution of log messages by exploring word-word interactions features. Firstly, we compute the interactive information of each word-word pair in the input log sequence, constructing self-matching attention vectors. Next, we use these self-matching attention vectors to manage the log sequence and construct the representation vectors. Hence, the log sequence can be matched word-by-word, adapting to the evolution of log messages. In addition, we combine pre-trained models in our proposed network to generate the higher-level semantic component information. More importantly, we use a low-rank bi-linear pooling approach to connect inconsistent and compositional information, thus our model can reduce potential information redundancy without weakening the discriminative ability. Experiment results on publicly available datasets demonstrate that our model significantly outperforms extant baselines on standard evaluation metrics, including precision, recall, F1 score and accuracy.