A Comparative Study of Unsupervised Models for Anomaly Detection in Maritime AIS Data

Title

A Comparative Study of Unsupervised Models for Anomaly Detection in Maritime AIS Data

Creator

Srivathsan, R. Abhijit; Sivakumar, R.; Murugesan, Ranjani

Description

The integrity of global maritime trade is increasingly threatened by deceptive practices such as sanctions evasion and illicit trafficking, often facilitated by the manipulation of vessel tracking data from the Automatic Identification System (AIS). While AIS provides a rich source for monitoring vessel behavior, the vast scale of the data and the novelty of anomalous patterns necessitate advanced, automated detection methods. This paper presents a comprehensive benchmarking study of four dis-tinct unsupervised machine learning architectures for detecting anomalies in historical AIS vessel trajectories. The evaluated models include a Bidirectional GRU (Bi-GRU) autoencoder, a probabilistic GeoTrackNet with A Contrario detection, a two-level grid representation with Isolation Forest, and a multi-model approach combining spatial-thematic attributes with Isolation Forest. We provide detailed mathematical formulations, algorithmic descriptions, and rigorous comparative analysis of each approach, examining trade-offs between temporal modeling, spa-tial context awareness, feature engineering, and computational complexity. Our benchmarking results on 985,700 AIS messages indicate that spatially-aware models (GeoTrackNet, grid-based methods) demonstrate significantly higher sensitivity (6.76%-10.00% anomaly rates) than purely temporal models (0.20%), but at greater computational cost. This study provides practical guidance for model selection based on operational requirements and proposes future directions toward multimodal architectures integrating trajectory analysis with document-based verification. 2025 IEEE.

Source

4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.881-888

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

AIS; Anomaly Detection; Benchmarking Study; Deep Learning; Illicit Trade; Isolation Forest; Maritime Security; Unsu-pervised Learning

Coverage

Srivathsan R.A., Christ (Deemed to be University), Bangalore, India; Sivakumar R., Christ (Deemed to be University), Bangalore, India; Murugesan R., Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833156587-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAAIC64647.2025.11330876

https://www.scopus.com/pages/publications/105034879292?origin=resultslist