Transformer-Based Anomaly Detection Using Log Sequence Embeddings

This paper introduces a flexible Transformer-based model for detecting anomalies in system logs. By embedding log templates with a pre-trained BERT model and incorporating positional and temporal encoding, it captures both semantic and sequential context within log sequences. The approach supports variable sequence lengths and configurable input features, enabling extensive experimentation across datasets. The model performs supervised binary classification to distinguish normal from anomalous patterns, using a [CLS]-like token for sequence-level representation. Overall, it pushes the boundaries of log-based anomaly detection by integrating modern NLP and deep learning techniques into system monitoring.