Clément Iphar

Detection of false AIS messages for the improvement of maritime situational awareness

The Automatic Identification System (AIS) was initially designed for safety and security of navigation purposes. However it was progressively also used for other objectives, such as surveillance, and thus led to the discovery of behaviors such as the falsification of the AIS messages by people that have been carrying out illegal activities and will to keep their activities up in an hidden way. In addition, the messages contain erroneous data and undergo spoofing attacks. The paper introduces the quality dimensions of data that shall be used in a quality assessment of AIS messages, in order to point out the dubious ones. The principles of a methodological approach for the detection of such data errors and falsifications are introduced.

On the Interest of Data Mining for an Integrity Assessment of AIS Messages

Put in place by the International Maritime Organization, the Automatic Identification System is a worldwide maritime electronic system that sends radio broadcasted messages at a high rate between the stations, either on board the vessels or on shores. However, some misuses of the system such as identity theft, localization spoofing or disappearances have been demonstrated. The high rate of transmission implies a considerable amount of data to process in order to point out those irregularities. This paper proposes a method based on data mining and clustering methods combined to an integrity assessment of AIS messages for anomaly detection, with a proposition of software architecture for a data processing done both on-the-fly and with archived data. The computation of confidence coefficients and the use of data mining techniques will lead to behaviour characterization with the purpose of enhance the maritime situational awareness.