Multi-Scale Temporal Convolutional Networks and Multi-Head Attention for Robust Log Anomaly Detection

Abstract

System logs are instrumental in understanding computer system behavior and ensuring system stability and reliability, making anomaly detection in system logs crucial. However, with the increasing scale and complexity of modern software systems, log data is growing exponentially, rendering traditional manual log inspection methods inefficient. Moreover, the evolution of log messages over time results in a lower accuracy rate for anomaly detection. To address these issues, this paper proposes a log anomaly detection method based on multi-scale temporal convolution networks and multi-head attention. This method utilizes temporal convolution networks to extract temporal information from log data and extracts hidden features of logs through different receptive fields of multi-scale convolution kernels. By integrating the multi-head attention mechanism, the sequential dependencies of logs can be better captured. We conducted repeated experiments on the authoritative public HDFS and BGL log datasets to evaluate their detection accuracy and robustness. The experiments demonstrate that MTCNLog outperforms existing anomaly detection methods and is robust to the continuous evolution of logs.