MMSI Based Anomaly Classification in AIS: A Rules-First Baseline from International Numbering Standards

Research on Automatic Identification System (AIS) anomalies has largely focused on vessel trajectories and kinematics, while identifier validity is often assumed. This study fills that gap by using the nine-digit Maritime Mobile Service Identity (MMSI) to build a rules-first baseline for anomaly classification. Validation rules are derived from ITU-R M.585-9 and operational guidance (USCG NA VCEN, AMSA), covering format constraints, category and prefix patterns, and MID ranges. The same rules are applied to two public data sources (Global Fishing Watch and NOAA/Access AIS). The pipeline assigns per-record labels for validity, category, and diagnostic notes, and defines a taxonomy of identity anomalies: invalid format, misclassification or misuse, MID and policy inconsistencies, and spatiotemporal “cloned MMSI” detected via overlap tests when positions are available. Results indicate that identity screening reduces noise, highlights priority cases, and produces cleaner inputs for downstream behavioral models without relying on speed or trajectories. Contributions include a reproducible MMSI rule set, an anomaly taxonomy, and a per-source evaluation protocol to avoid misleading generalizations. The approach is transparent, computationally efficient, and easy to integrate as a first-stage filter in maritime analytics pipelines.