Interpretable Ordinal-Aware With Contrastive-Enhanced Anomaly Severity Detection on UAV Flight Log Messages

Detecting abnormalities in a system using log records is a common approach, although such applications may vary between different deployment domains. For example, in drone forensics, existing studies generally consider anomalies as contradictory to normal events. In other words, all types of anomalies are considered equally important. However, the peculiar events experienced by a drone during a flight might contain distinct features indicating the anomalies’ importance or level of urgency. For instance, the message “Critically low power. Aircraft is landing” is more important than “Forward Obstacle Sensing not Working”. This study proposes DroneLog, an interpretable framework for detecting anomalies and the severity levels on drone flight logs, using a multitask learning approach in a unified pipeline. Complying with the multitask learning nature, two target label representations are designed to leverage the shared low-level common features of the input messages over various severity levels. An extensive experiment with a significant hyperparameter search space is performed to find the best-performing model to compare with several baselines and state-of-the-art methods from previous studies. The integrated gradient is employed to investigate the best-performing model’s trustworthiness and interpretability. We found that the model perceived the log messages’ semantics differently and were not aligned with how a human expert would. Therefore, DroneSBERT, a domain-specific embedding trained using contrastive learning, is proposed as a follow-up to the interpretability findings. From the experimental results, the proposed method achieves the highest performance with an improvement in accuracy and F1 scores of 5.128% and 4.453% on the unique-filtered dataset, respectively. Therefore, the proposed method accordingly addresses the task objective with a promising performance while being interpretable and trustworthy.