Early Classification of Intentions for Maritime Domains Using Deep Learning Models

Effective detection of intentions in a maritime domain has become more crucial for the enhancement of the maritime security systems. Predicting intentions accurately based on a set of predictive attributes in the naval domain can guide strategic decision-making capabilities, which reduces the likelihood of relative incidents. In this paper, we focus on developing a deep learning-based framework for the immediate classification of both hostile and non-hostile behaviors in the maritime domain to minimize the potential threats. For this process, first, we collected maritime datasets for different ship navigation behaviors. Second, we developed a set of features from the raw vessel data. Third, we applied different feature selection methods to identify the optimal subset of features, and finally we developed and evaluated the performance of two deep learning models for these different feature subsets. The experimental results show that by using the optimal feature subset our proposed deep learning models can effectively identify seven distinct navigation behaviors, with an overall accuracy of 97% in a maritime domain. On the other hand, our proposed model can distinguish between Hostile and Non-Hostile behaviors with an overall accuracy of 99%.