Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance

TL;DR

This paper introduces context-aware autoencoders that incorporate vessel-specific context to enhance anomaly detection accuracy in maritime surveillance, outperforming traditional autoencoders in identifying fishing status anomalies.

Contribution

The paper presents a novel context-aware autoencoder framework that integrates context-specific thresholds, improving anomaly detection in maritime vessel traffic over conventional methods.

Findings

Context significantly affects reconstruction loss and anomaly detection.

Context-aware autoencoders outperform conventional autoencoders in detecting anomalies.

The approach reduces computational costs while increasing detection accuracy.

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.