TASA:Template-driven Log Anomaly Detection with Variable Integration and Sparse Attention
With the advance of computing and network technologies,the scale and complexity of computer application sys-tems have been continuously increasing,leading to a rapid growth in the volume and variety of system log data.Consequently,i-dentifying log anomalies has become a significant challenge in ensuring the security of complex systems.However,existing rule-based or machine learning-based log anomaly detection methods have limitations,such as ignoring log variables,insufficient ex-traction of log semantic features,and poor performance in detecting new types of logs.To address these issues,this paper proposes a novel deep learning-based log anomaly detection model—template-driven log anomaly detection with variable integration and sparse attention.The model integrates template and variable information from log data and introduces a sparse attention mecha-nism,demonstrating excellent performance in handling long sequences of logs.It effectively captures and represents the overall characteristics of sequences.Not only can the model understand the semantics of log variables,but it can also effectively detect a-nomalous behaviors in log sequences.Experimental results show that the model exhibits high performance on three open-source datasets.
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