An Overview of Log-Based Anomaly Detection Techniques

This article explores various formulations and methodologies for log-based anomaly detection, including binary classification, prediction, masked log modeling, and clustering. It contrasts supervised and unsupervised approaches, highlighting trade-offs between labeled accuracy and real-world practicality. The paper reviews how contextual, sequential, temporal, and semantic information from log data influences detection accuracy and discusses empirical studies comparing traditional versus deep-learning methods. Ultimately, the research proposes a Transformer-based anomaly detection model capable of capturing richer log features, offering a more holistic understanding of how AI identifies system anomalies across diverse datasets.