Malicious AIS Spoofing and Abnormal Stealth Deviations: A Comprehensive Statistical Framework for Maritime Anomaly Detection

• Published in: IEEE transactions on aerospace and electronic systems Vol. 57; no. 4; pp. 2093 - 2108
• Main Authors: d'Afflisio, Enrica, Braca, Paolo, Willett, Peter
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Artificial intelligence; Automatic identification system (AIS); Collision avoidance; Data models; data spoofing; Deviation; Hypothesis testing; Likelihood ratio; maritime anomaly detection; maritime security; maritime situational awareness; Model testing; model-order selection (MOS); multiple statistical hypothesis test; Ornstein–Uhlenbeck (OU) process; Radar; Radar tracking; Spoofing; Surveillance; target tracking; Testing; Trajectory
• DOI: 10.1109/TAES.2021.3083466
• ISSN: 0018-9251

The automatic identification system (AIS) is an essential and economical equipment for collision avoidance and maritime surveillance. However, AIS can be subject to intentional reporting of false information, or “spoofing”. This article assumes the vessel trajectory nominally follows a piecewise mean-reverting process; thereby, it addresses the problem of establishing whether a vessel is reporting adulterated position information through AIS messages in order to hide its current planned route and a possible deviation from the nominal route. Multiple hypothesis testing suggests a framework to enlist reliable information from monitoring systems (coastal radars and space-born satellite sensors) in support of detection of anomalies, spoofing, and stealth deviations. The proposed solution involves the derivation of anomaly detection rules based on the generalized likelihood ratio test and the model-order selection methodologies. The effectiveness of the proposed anomaly detection strategy is tested for different case studies within an operational scenario with simulated data.