Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srivathsan, R. Abhijit; Sivakumar, R.; Murugesan, Ranjani Title A name given to the resource A Comparative Study of Unsupervised Models for Anomaly Detection in Maritime AIS Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.881-888 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11330876" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11330876</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034879292?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034879292?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource AIS; Anomaly Detection; Benchmarking Study; Deep Learning; Illicit Trade; Isolation Forest; Maritime Security; Unsu-pervised Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online