Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance

2026-01-27

AI Summary

This work addresses the challenge of detecting collective and context-dependent anomalies in maritime surveillance, which traditional autoencoders struggle to capture due to their inability to leverage vessel-specific contextual information such as AIS messages. To overcome this limitation, the authors propose a context-aware autoencoder that, for the first time, integrates context-specific thresholds into the autoencoder framework, dynamically adjusting the reconstruction loss criterion. By jointly modeling contextual dependencies, temporal dynamics, and AIS data characteristics, the method significantly enhances the detection of anomalous fishing vessel behaviors. The approach not only reduces computational overhead but also outperforms conventional anomaly detection techniques, thereby demonstrating the critical role of contextual information in refining reconstruction error modeling and improving detection performance.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The detection of anomalies is crucial to ensuring the safety and security of maritime vessel traffic surveillance. Although autoencoders are popular for anomaly detection, their effectiveness in identifying collective and contextual anomalies is limited, especially in the maritime domain, where anomalies depend on vessel-specific contexts derived from self-reported AIS messages. To address these limitations, we propose a novel solution: the context-aware autoencoder. By integrating context-specific thresholds, our method improves detection accuracy and reduces computational cost. We compare four context-aware autoencoder variants and a conventional autoencoder using a case study focused on fishing status anomalies in maritime surveillance. Results demonstrate the significant impact of context on reconstruction loss and anomaly detection. The context-aware autoencoder outperforms others in detecting anomalies in time series data. By incorporating context-specific thresholds and recognizing the importance of context in anomaly detection, our approach offers a promising solution to improve accuracy in maritime vessel traffic surveillance systems.

Problem

• anomaly detection
• maritime surveillance
• context-aware
• autoencoder
• AIS

Innovation

• context-aware autoencoder
• anomaly detection
• maritime surveillance
• AIS data
• context-specific thresholds