Detecting Log Anomalies with Multi-Head Attention (LAMA)

Understand

Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas.

• In this work, we explore the task of log anomaly detection (especially computer system logs and user behavior logs) by analyzing logs' sequential information.
• We propose LAMA, a multi-head attention based sequential model to process log streams as template activity (event) sequences.
• A next event prediction task is applied to train the model for anomaly detection.