Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAVXr2_uyvw9i6LNPO3DQHcDBw54LjcEd_l7-g6THj-9E/assertion>. }
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf description "The surveillance of maritime traffic is confronted with very important difficulties in detecting illegal activities at sea. In this article, we present the first results of a self-supervised learning method which aims to detect voluntary disconnec- tions of the identification’ system of vessels. By processing data from four Norwegian surveillance satellites, our lear- ning model aims to identify vessels suspected of illegal acti- vities such as fishing in protected areas or crossing econo- mic exclusion zones in real time. In this article, we present an approach based on self-supervised learning techniques, and experienced from real data." assertion.
- response-ais.html description "VTexplorer API (https://www.vtexplorer.com) documentation. This documentation can be useful to understand how AIS data can be processed." assertion.
- tle-fmt.php description "This document describes the NORA Two-Line Element Set Format (TLE) where data for each satellite consists of three lines with a fixed format (see document)." assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a description "In transport infrastructures, vessel traffic services, air traffic management, and connected cars all rely on unauthenticated and unencrypted messages transfer that renders these services vulnerable to cyberattacks. Typical attacks such as False Data Injection Attacks (FDIA) are challenging to detect as they alter the semantics of the data (e.g., by adding/removing/multiplying elements on real-time control equipment), while preserving the syntactical correctness of the messages. Identifying these attacks and classifying them as serious threats or unintentional false data has become a significant challenge of traffic monitoring authorities. The TSAR project aims at demonstrating that recent advances in Artificial Intelligence (AI) can be leveraged in the automatic detection of FDIA in transport infrastructures. By combining realistic threat data generation based on constraint-based software testing techniques and automatic detection with deep reinforcement learning, TSAR will propose a new technology for automatic FDIA generation and detection. This technology will be empirically evaluated with end-users from the maritime domain and with open and accessible data in two other domains, namely air traffic control, and connected cars. By leveraging automatic detection of FDIA in traffic management systems, TSAR will also prepare the ground for the upcoming revolution in traffic management, which concerns, self-driving vessels, self-driving aircraft, and self-driving cars." assertion.
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- 7998d851-41e8-4c51-aa06-deff6fd5f09a cite-as "Pierre Bernabé, Anne Fouilloux, Jørgen Schartum Dokken, Thomas Roehr, and Dusica Marijan. "T-SAR project." ROHub. Oct 04 ,2022. https://w3id.org/ro-id/7998d851-41e8-4c51-aa06-deff6fd5f09a." assertion.
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