TY  - EJOU
AU  - Zhang, Chexiaole 
AU  - Fu, Haiyan 

TI  - LogDA: Dual Attention-Based Log Anomaly Detection Addressing Data Imbalance
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 1
SN  - 1546-2226

AB  - As computer data grows exponentially, detecting anomalies within system logs has become increasingly important. Current research on log anomaly detection largely depends on log templates derived from log parsing. Word embedding is utilized to extract information from these templates. However, this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing. Currently, specialized research on data imbalance across log template categories remains scarce. A dual-attention-based log anomaly detection model (LogDA), which leveraged data imbalance, was proposed to address these issues in the work. The LogDA model initially utilized a pre-trained model to extract semantic embedding from log templates. Besides, the similarity between embedding was calculated to discern the relationships among the various templates. Then, a Transformer model with a dual-attention mechanism was constructed to capture positional information and global dependencies. Compared to multiple baseline experiments across three public datasets, the proposed approach could improve precision, recall, and F1 scores.
KW  - Anomaly detection; system log; deep learning; transformer; neural networks

DO  - 10.32604/cmc.2025.060740