Proactive Maritime Threat Prediction: Vessel Intent Classification with LSTMs and Transformers Using a Sliding Window Approach

Maritime safety has long been a major concern, with significant efforts dedicated to improving it. Early prediction of hostile behaviors from nearby ships can greatly enhance maritime security. Machine learning has proven valuable in related research areas, such as driving behavior prediction and naval trajectory forecasting. In our previous studies, we highlighted how deep learning could be applied to detect the behavior of nearby vessels. In this research, we take it a step further by predicting future behavior using a sliding window approach. We conduct binary classification to determine whether the behavior of external ships is hostile or benign, followed by multiclass classification to identify specific types of hostile or benign behaviors. We experiment with both a large, unbalanced dataset and a balanced subset to assess performance variations. Our results show that all RNN-based deep learning models perform significantly better on the balanced dataset, while the transformer model achieves superior performance on the full dataset. In binary classification, the transformer model achieves the best performance on the full dataset, with an F1-score of 89.48%, whereas the Bidirectional LSTM (Bi-LSTM) model performs best on the balanced dataset, with an F1-score of 90.08%. In multiclass classification, the Bi-LSTM model attains the highest performance, achieving an F1-score of 87.93% on the balanced dataset and 81.41% on the full dataset. This research demonstrates that deep learning models can effectively predict the behavior of nearby ships in advance, enhancing maritime safety by enabling timely preventive actions.