A Deep Learning Approach for Identifying Intentional AIS Signal Tampering in Maritime Trajectories - Xiangdong Lv; Ruhao Jiang; Chao Chang; Nina Shu; Tao Wu (Journal of Marine Science and Engineering)
In the field of maritime safety research, ship behavior analysis is usually based on data provided by automatic identification systems (AISs). Prevailing studies predominantly focus on detecting the behaviors of vessels that may affect maritime safety, especially the abnormal disappearance of ship AIS signals, neglecting subsequent measures to trace these illegal ships. To fill this gap, we propose a deep learning model named multi-dimensional convolutional long short-term memory (MConLSTM) to tackle the challenge of recognizing ship trajectories in cases where AIS signals are intentionally altered. By employing a self-supervised approach, the model is trained using historical real-world data. Extensive experiments show that MConLSTM exhibits superior analytical capabilities when it comes to processing and analyzing AIS data. Notably, even in scenarios with scant training data, the model exhibits exceptional performance, with an average accuracy 22.74% higher than the general model. Finally, we validated the practical significance and feasibility of the proposed method by simulating real-world scenarios.