iGroup Learning and iDetect for Dynamic Anomaly Detection with Applications in Maritime Threat Detection

摘要

The maritime transportation system is critical to the US and world economy. This paper reports on two novel statistical tools, iGroup learning for individualized grouping and baseline distribution formation, and iDetect for subsequent individualized detection of anomalous deviations from the baseline distribution. These statistical methods are being developed, tested, and implemented in the context of maritime threat detection, but can be easily applied in other areas. In the maritime domain, the tools aim to provide early warnings of anomalies and assessments of resulting risk for vessels being monitored. The paper presents some preliminary results about these tools and specifically reports on a case study aimed at finding anomalous behavior for vessels approaching a port.