  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. Extensive empirical studies demonstrate that our new model
outperforms existing log anomaly detection methods including statistical and
deep learning methodologies, which validate the effectiveness of our proposed
method in learning sequence patterns of log data.
