# Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning

arXiv: https://arxiv.org/abs/2310.15586 · HTML: https://mlsanity.com/paper/2310.15586/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection](https://mlsanity.com/paper/1912.00682/) (2019)
- [A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams](https://mlsanity.com/paper/1806.03972/) (2018)
- [Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data](https://mlsanity.com/paper/2002.05013/) (2020)

## Built on by (most cited first, 5 of 5)

- [AIS Data-Driven Maritime Monitoring Based on Transformer: A Comprehensive Review](https://mlsanity.com/paper/2505.07374/) (2025)
- [Graph Learning-Driven Multi-Vessel Association: Fusing Multimodal Data for Maritime Intelligence](https://mlsanity.com/paper/2504.09197/) (2025)
- [Automatic Detection of Dark Ship-to-Ship Transfers using Deep Learning and Satellite Imagery](https://mlsanity.com/paper/2404.07607/) (2024)
- [Maritime Activities Observed Through Open-Access Positioning Data: Moving and Stationary Vessels in the Baltic Sea](https://mlsanity.com/paper/2511.23016/) (2025)
- [An Automated Tip-and-Cue Framework for Optimized Satellite Tasking and Visual Intelligence](https://mlsanity.com/paper/2512.09670/) (2025)

## Abstract (arXiv)

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.