Identification of suspicious behaviour through anomalies in the tracking data of fishing vessels

TL;DR

This paper explores how analyzing anomalies in AIS data, especially silence anomalies near coastlines, can help detect suspicious and potentially illegal fishing vessel activities, aiding maritime surveillance.

Contribution

It introduces a method to identify suspicious vessel behavior through anomaly detection in AIS data, emphasizing the significance of silence anomalies as indicators of illegal activities.

Findings

94.9% of silence anomalies occur within 100 km of land

Silence anomalies can serve as proxies for illegal fishing activities

High-resolution AIS data enhances anomaly detection capabilities

Abstract

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. This information is obtained after cleaning the data from errors of different natures. However, in the case of Automated Information Systems (AIS), attached to vessels, these errors can come from intentional manipulation of the electronic device. Thus, the analysis of anomalies can provide valuable information on suspicious behaviour. Here, we analyse anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silence anomalies, those occurring when positioning data is absent for more than 24 h, shows that they occur more likely closer to land, observing 94.9% of the anomalies at less than 100 km from the shore. This behaviour suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimise monitoring, control and surveillance measures.