MFLog: Log Sequence Anomaly Detection via Multifeature Extraction
Zhiying Cao, Peipeng Wang, Yanwei Qiu, Xuejie Wang, Xiuguo Zhang, Weigang Xu · Software Testing Verification and Reliability · 2025
ABSTRACT System logs play a vital role in recording operational behaviours and internal states. However, most of the research mainly focused on the log template features, without considering other features such as components and levels. Facing this challenge, we propose MFLog, a log sequence anomaly detection approach via multifeature extraction. Firstly, a log sequence feature extraction method based on the optimized BERT model and Bi‐LSTM is proposed, which adopts the idea of out‐of‐manifold regularization to optimize the BERT model, employs Mixup as a form of out‐of‐manifold regularization to impose linear constraints on the out‐of‐manifold input space of the model, and extracts the sequence features of the log templates by using the optimized BERT model. Then, we utilize Bi‐LSTM to analyse log components and levels, aiming to detect workflow‐related anomalies and distinguish logs across different severity levels. Secondly, we propose a method for log sequence anomaly detection using dynamic weights. This method adaptively integrates log template, component, and level features using dynamic weights, effectively addressing the issue of feature redundancy and reducing the computational complexity caused by direct feature concatenation. Comparative experiments conducted on two publicly available datasets demonstrate that MFLog outperforms several state‐of‐the‐art approaches in Precision, Recall and F1‐score.