Behavioral Model Anomaly Detection in Automatic Identification Systems (AIS)

Over 90% of all goods in the world, at some point in their life, are on a vessel at sea. Currently, the maritime industry relies on the Automatic Identification System (AIS) for collision avoidance and vessel tracking. AIS is an unencrypted, unauthenticated protocol that is vulnerable to various types of cyber attacks allowing malicious actors to alter the location of vessels. With the advent of the Ocean of Things (OoT), vessels are sharing more information than vessel location alone at sea. Increasingly, more information is becoming critical for safe and efficient operation at sea. This method is a novel approach of applying machine learning to build vessel behavior models that exploits such information. These models will allow vessels to detect anomalous communication from vessels nearby. This will enable vessels to determine the quality of the message shared between each other and, more critically, identify malicious actors.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Coleman, Jacob

Contributors dc:contributor

• Kandah, Farah
• Skjellum, Anthony; Tanis, Craig
• College of Engineering and Computer Science

• Anomaly detection (Computer security)
• Machine learning
• Ships -- Automatic identification systems

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/656

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1819