Detecting drug transfers via the drop-off method: A supervised model approach using AIS data

Maritime security is of tremendous importance in countering drug trafficking, particularly through sea-based routes. In this paper, we address the pressing need for effective detection methods by introducing a novel approach utilizing Automatic Identification System (AIS) data. Our focus lies on detecting the ‘drop-off’ method, a prevalent technique for contraband smuggling at sea. Unlike existing research, primarily employing unsupervised methods, we propose a supervised model specifically tailored to this illicit activity, with a particular emphasis on its application to fishing vessels. Our model significantly reduces the number of data points requiring classification by the observer by 70% , thereby enhancing the efficiency of the drop-off detection process. By employing a Long Short-Term Memory (LSTM) model, our approach demonstrates a change from traditional methods and offers advantages in capturing complex temporal patterns inherent in ‘drop-off’ activities. The rationale behind choosing LSTM lies in its ability to effectively model sequential data, which is essential for detecting drug traffic activities at sea where patterns are subtle and dynamic. Moreover, this model holds the potential for integration into real-time surveillance systems, thereby enhancing operational capabilities in detecting and preventing drug traffic. The generalizability of our model makes for considerable potential in enhancing maritime security efforts and providing assistance in countering drug traffic on a global scale. Importantly, our model outperforms both baseline models, underscoring its effectiveness and superiority in addressing the specific challenges posed by ‘drop-off’ detection. For more information and access to the code repository, please visit this link.

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