Automated Identification System (AIS) comprises of a global real-time network of maritime vessels, transmitters, and satellite or ground-station receivers, which are used to track and record time-stamped static (vessel-id, type, etc.) and dynamic (location, course, speed, etc.) information of all maritime vessels. Traditionally, this data feed has been used by vessel traffic services to avoid vessel collisions, however increasingly, marine researchers and data scientists have shown interest in developing algorithms on historical AIS data-dumps with diverse objectives such as estimating trends in global ocean-currents, maritime logistical planning, fishing fleet monitoring and enhancing maritime security. This paper aims to propose a novel shipping-route extraction algorithm to process historical AIS data (running into tens of gigabytes of daily data, from more than a hundred thousand maritime vessels), to effectively characterize global shipping route behaviour and capture its seasonal trends. This is done by formulating a sigmoid based turning waypoint identification, which does not solely depend on the change in the course of a vessel, derived from AIS message. The AIS data is further processed to excerpt the vessel’s distance from the nearest port and nearest shore with resolution of about 1 km, which forms a handy metric for the identification of suspicious ports. Several other use cases are also explored, which include detecting various anomalies in individual vessel behaviour such as intentional or unintentional switching off AIS transmission, shipping-lane deviations, spoofing transmission messages. To tackle spoofing anomaly, various spoofing identification methods are discussed. A region-based spoofing identification algorithm is developed to identify spoofing points under multiple categories using a clustering algorithm. All the results are visualized through an open-source map overlay. The results contribute to a growing body of fast shipping route analysis algorithms.
Maritime Vessel Route Extraction and Automatic Information System (AIS) Spoofing Detection
2021-02-19
3856570 byte
Conference paper
Electronic Resource
English
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