DATA QUALITY ASSESSMENT FOR MARITIME SITUATION AWARENESS

Iphar, C.; Napoli, A.; Ray, C. · 2015 · Crossref

DOI: 10.5194/isprsannals-ii-3-w5-291-2015

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Summary

This paper addresses the critical issue of data quality within the Automatic Identification System (AIS), a global maritime surveillance network originally designed for safety and collision avoidance. As AIS usage has expanded to include traffic management, logistics, and security monitoring, the system’s vulnerabilities have become increasingly significant. The authors highlight that AIS data is neither authenticated nor encrypted, making it susceptible to unintentional errors, voluntary falsification by vessel crews, and external spoofing. These data integrity issues compromise maritime situational awareness, potentially leading to erroneous decisions by Vessel Traffic Services (VTS) and onboard crews, with serious implications for safety, security, and environmental protection. The paper provides a comprehensive review of the AIS architecture, detailing its transmission protocols, message types, and the distinction between Class A and Class B transponders. It categorizes data quality issues into three main types: unintentional errors, intentional falsification, and external spoofing. Unintentional errors arise from manual entry mistakes or system limitations; for instance, studies cited indicate that 2% of Maritime Mobile Service Identity (MMSI) numbers are erroneous, 47% of vessel lengths are discrepant, and 30% of navigation statuses are incorrect. Intentional falsification includes identity theft, where vessels use another ship’s MMSI to evade sanctions, and destination masking to obscure illegal activities. External spoofing involves malicious actors transmitting false signals to create ghost vessels, trigger false collision alerts, or disrupt navigation, as demonstrated by experiments spelling words on tracking websites or simulating search and rescue alerts. To address these challenges, the authors propose a novel methodological approach for assessing data quality and integrity. This approach is inspired by Information Theory, specifically Shannon’s work, to compute a coefficient that rates the reliability and integrity of AIS messages. The assessment operates at multiple levels: internal message integrity, consistency between groups of messages, and the relationship between a message and the broader dataset. The proposed framework aims to differentiate between unintentional errors and malicious falsifications by analyzing specific data fields and their likelihood of manipulation. Additionally, the paper outlines a risk modeling strategy using ontologies—trajectory, geographic, and domain-specific—to analyze threat processes and the severity of potential consequences. The significance of this work lies in its contribution to enhancing maritime situational awareness through rigorous data quality assessment. By implementing a system that can detect dubious messages and distinguish between error types, decision-makers can improve the reliability of their situational analysis. The authors conclude that such an assessment mechanism is essential for maintaining trust in the AIS system, preventing the distortion of global traffic views, and ensuring the safety and security of maritime operations. The research lays the groundwork for future developments in real-time data analysis and risk modeling, aiming to mitigate the risks posed by an unsecured and unauthenticated surveillance infrastructure.

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• situation awareness theory