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Behavioral Model Anomaly Detection in Automatic Identification Systems (AIS)
تشخیص ناهنجاری مدل رفتاری در سیستم های شناسایی خودکار (AIS)-2020 Over 90% of all goods in the world, at some point in
their life, are carried on a vessel at sea. Currently, the maritime
industry relies on the Automatic Identification System (AIS) for
collision avoidance, vessel tracking, and vessel awareness while
operating at sea. AIS is a plaintext, unencrypted, unauthenticated
protocol and, as such, is vulnerable to various types of attacks.
Malicious actors can alter the AIS location of a vessel by
spoofing a vessel or alter the channel the AIS receiver is using
to send nefarious information to a vessel privately. With the
advent of the Ocean of Things (OoT), vessels are sharing more
information than vessel location alone at sea. As this information
becomes critical for safe and efficient operation at sea, we in this
work present a novel approach of applying machine learning
to build behavior models for vessels at sea. These models allow
vessels to detect anomalous communication from vessels nearby,
thus enable vessels to determine the quality of the messages
shared between each other and, more critically, identify malicious
vessels’ behaviors. Keywords: Automatic Identification System | machine learning | behavioral model | anomaly detection | Ocean of Things |
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