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Maritime Anomaly Detection: The use of AIS data to identify Dark Activity
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| 3.05 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Maritime security is critical to global trade, transportation, and defence. It necessitates
advanced methods for monitoring and detecting abnormal behaviours, especially "dark
activity”, where vessels deactivate their Automatic Identification System (AIS) transponders
to evade detection. This research aims to develop robust machine learning methodologies to
identify anomalies within AIS data, focusing on detecting dark activity. The study leverages a
comprehensive dataset comprising real AIS data from 2020, focusing on the Mediterranean
Sea, provided by the Navy Information Analysis and Management Department (DAGI) and the
National Maritime Authority (AMN). This dataset, consisting of over 330,000 entries, presents
a significant challenge due to its highly imbalanced nature, with instances of dark activity
constituting a mere fraction of the total data. Overcoming this imbalance was crucial to the
success of the research. Advanced preprocessing techniques such as oversampling and
synthetic sampling were essential to prevent the models from being biased towards the
majority class and to ensure effective learning of minority class patterns. This study employs
supervised and unsupervised machine learning methods to tackle different aspects of
anomaly detection. Supervised models were primarily used to classify dark activity instances,
while unsupervised models were implemented to detect general anomalies without using
predefined labels. Evaluation metrics focused on F1-Score and recall for supervised and
Silhouette Score for unsupervised methods. These models were deployed using FastAPI,
enabling real-time classification and anomaly detection from new AIS data. By addressing the
significant challenge posed by the highly imbalanced dataset and integrating advanced
machine learning techniques, this study’s findings demonstrate the potential of machine
learning in enhancing maritime surveillance, where advanced stacking methods were able to
classify dark activity cases with an outstanding level of certainty, making a substantial
contribution to naval security offering practical solutions for identifying and responding to
dark activities, ultimately enhancing the safety and security of naval operations
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Automatic Identification System (AIS) Anomaly Detection Dark Activity Imbalanced Data Machine Learning Maritime Security Supervised Learning Unsupervised Learning SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 14 - Life below water SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals