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Formalisation of a data analysis environment based on anomaly detection for risk assessment : Application to Maritime Domain Awareness

Abstract : At sea, various systems enable vessels to be aware of their environment and on the coast, those systems, such as radar, provide a picture of the maritime traffic to the coastal states. One of those systems, the Automatic Identification System (AIS) is used for security purposes (anti-collision) and as a tool for on-shore bodies as a control and surveillance and decision-support tool.An assessment of AIS based on data quality dimensions is proposed, in which integrity is highlighted as the most important of data quality dimensions. As the structure of AIS data is complex, a list of integrity items have been established, their purpose being to assess the consistency of the data within the data fields with the technical specifications of the system and the consistency of the data fields within themselves in a message and between the different messages. In addition, the use of additional data (such as fleet registers) provides additional information to assess the truthfulness and the genuineness of an AIS message and its sender.The system is weekly secured and bad quality data have been demonstrated, such as errors in the messages, data falsification or data spoofing, exemplified in concrete cases such as identity theft or vessel voluntary disappearances. In addition to message assessment, a set of threats have been identified, and an assessment of the associated risks is proposed, allowing a better comprehension of the maritime situation and the establishment of links between the vulnerabilities caused by the weaknesses of the system and the maritime risks related to the safety and security of maritime navigation.
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  • HAL Id : tel-01783958, version 1

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Clément Iphar. Formalisation of a data analysis environment based on anomaly detection for risk assessment : Application to Maritime Domain Awareness. Library and information sciences. Université Paris sciences et lettres, 2017. English. ⟨NNT : 2017PSLEM041⟩. ⟨tel-01783958⟩

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