Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RAVXr2_uyvw9i6LNPO3DQHcDBw54LjcEd_l7-g6THj-9E/assertion>. }
- vesseltype_identification_dae name "vesseltype_identification_dae (gitlab private repo)" assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf name "Self-supervised learning for the detection of illegal actions during maritime traffic monitoring" assertion.
- ro-crate-metadata.json conformsTo 1.1 assertion.
- 0000-0002-1784-2920 email "annef@simula.no" assertion.
- 00vn06n10 email "post@simula.no" assertion.
- mailto:dusica@simula.no email "dusica@simula.no" assertion.
- mailto:pierbernabe@simula.no email "pierbernabe@simula.no" assertion.
- 0000-0001-6489-8858 email "dokken@simula.no" assertion.
- 0000-0002-7715-7052 email "roehr@simula.no" assertion.
- 0000-0002-1784-2920 affiliation "Simula Research Laboratory" assertion.
- mailto:dusica@simula.no affiliation "Simula Research Laboratory" assertion.
- mailto:pierbernabe@simula.no affiliation "Simula Research Laboratory" assertion.
- 0000-0001-6489-8858 affiliation "Simula Research Laboratory" assertion.
- 0000-0002-7715-7052 affiliation "Simula Research Laboratory" assertion.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a author 0000-0002-1784-2920 assertion.
- response-ais.html author 0000-0002-1784-2920 assertion.
- tle-fmt.php author 0000-0002-1784-2920 assertion.
- edit?usp=sharing author 0000-0002-1784-2920 assertion.
- view?usp=sharing author 0000-0002-1784-2920 assertion.
- view?usp=sharing author 0000-0002-1784-2920 assertion.
- tsar-project author 0000-0002-1784-2920 assertion.
- 0AI6umItIl7BxUk9PVA author 0000-0002-1784-2920 assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a author 0000-0002-1784-2920 assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a author mailto:pierbernabe@simula.no assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a author 0000-0001-6489-8858 assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a author 0000-0002-7715-7052 assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 author 0000-0002-1784-2920 assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 author 0000-0002-1784-2920 assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 author 0000-0002-1784-2920 assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe author 0000-0002-1784-2920 assertion.
- jmse10010112 author 0000-0002-1784-2920 assertion.
- Two-line_element_set author 0000-0002-1784-2920 assertion.
- master author 0000-0002-1784-2920 assertion.
- marivisu-v2 author 0000-0002-1784-2920 assertion.
- pre-processing author 0000-0002-1784-2920 assertion.
- vesseltype_identification_dae author 0000-0002-1784-2920 assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf author 0000-0002-1784-2920 assertion.
- 3949 description "" assertion.
- c_a935cf3f description "" assertion.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a description "Research Object with sample AIS data (in-situ)" 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.
- edit?usp=sharing description "Main document (Google doc) provided when willing to start with TSAR Overview." assertion.
- view?usp=sharing description "Slides (private) presenting the T-SAR project." assertion.
- view?usp=sharing description "In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- tsar-project description "Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown." assertion.
- 0AI6umItIl7BxUk9PVA description "Internally shared google drive with data and documents for T-SAR project" 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.
- 5bffb6c2-45e3-4a80-9242-5afd61c21063 description "papers, conference proceeding generated by the TSAR project." assertion.
- 636a64a4-ba87-4d16-8a67-455bfd74e7d6 description "Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite)." assertion.
- bc6ab0f0-c443-4b11-a772-03fbcdb452c4 description "Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite)." assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 description "Distribution of samples on the surface of the globe." assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 description "The NorSat-3 microsatellite will be launched into space during spring 2021 with a radar detector developed at the Norwegian Defence Research Establishment (FFI). It will provide improved surveillance capability of the shipping traffic in Norwegian national waters. File downloaded from the Norwegian Defence Research Establishment (https://publications.ffi.no/nb/item/asset/dspace:7059/FFI-Facts_NorSat_Engelsk_web_v2.pdf)." assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 description "Major transportation surveillance protocols have not been specified with cyber securityin mind and therefore provide no encryption nor identification. These issues expose air and seatransport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fakesurveillance messages to dupe controllers and surveillance systems. There has been growing interestin conducting research on machine learning-based anomaly detection systems that address these newthreats. However, significant amounts of data are needed to achieve meaningful results with this typeof model. Raw, genuine data can be obtained from existing databases but need to be preprocessedbefore being fed to a model. Acquiring anomalous data is another challenge: such data is muchtoo scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the AutomaticIdentification System (AIS). Crafting anomalous data by hand, which has been the sole methodapplied to date, is hardly suitable for broad detection model testing. This paper proposes an approachbuilt upon existing libraries and ideas that offers ML researchers the necessary tools to facilitatethe access and processing of genuine data as well as to automatically generate synthetic anomaloussurveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of theapproach by discussing work in progress that includes the reproduction of related work, creation ofrelevant datasets and design of advanced anomaly" assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe description "This Research Object contains AIS data (raw and pre-processed by Statsat AS, Norway). It is not public and has been provided by Statsat AS (Norway). If you are working at Simula, information on where to find pre-processed data on the EX3 is given in the Data RO (README.txt in the metadata folder). This dataset has been used for developing new machine learning algorithms for detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway) led by Simula Research Laboratory (Oslo, Norway)." assertion.
- jmse10010112 description "The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations." assertion.
- Two-line_element_set description "Description of Two-Line Element Set (TLE) from Wikipedia." assertion.
- master description "Gitlab repository set up for reproducibility purposes." assertion.
- marivisu-v2 description "Marivisu serves as a demonstrator of the machine learning model developed to detect anomalies in the vessel trajectory. This work was supported by the Norwegian Research Council (RCN) TSAR project under contract 287893. Satellite AIS data used for model development and testing has been made available courteously by its owner, the Norwegian Coastal Administration (Kystverket)." assertion.
- pre-processing description "n maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transhipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) messages transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. The results show that the method can detect confirmed real-world intentional AIS shutdown operations." assertion.
- vesseltype_identification_dae description "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" 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.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a contentUrl "https://w3id.org/ro-id/88fba8bd-f2f0-402e-8147-b73b71e8691a" assertion.
- response-ais.html contentUrl "https://api.vtexplorer.com/docs/response-ais.html" assertion.
- tle-fmt.php contentUrl "https://celestrak.org/NORAD/documentation/tle-fmt.php" assertion.
- edit?usp=sharing contentUrl "https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing" assertion.
- view?usp=sharing contentUrl "https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing" assertion.
- view?usp=sharing contentUrl "https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing" assertion.
- tsar-project contentUrl "https://hackmd.io/@simula/tsar-project" assertion.
- 0AI6umItIl7BxUk9PVA contentUrl "https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA" assertion.
- 7998d851-41e8-4c51-aa06-deff6fd5f09a contentUrl "https://api.rohub.org/api/ros/7998d851-41e8-4c51-aa06-deff6fd5f09a/crate/download/" assertion.
- 3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2 contentUrl "https://api.rohub.org/api/resources/3e6f07ae-3da5-43ab-a7f9-4334ee01b8d2/download/" assertion.
- c7d9b7cb-7192-471b-82f4-13fe89dc6906 contentUrl "https://api.rohub.org/api/resources/c7d9b7cb-7192-471b-82f4-13fe89dc6906/download/" assertion.
- ed59cb0a-e359-4f95-932d-88375b08daa7 contentUrl "https://api.rohub.org/api/resources/ed59cb0a-e359-4f95-932d-88375b08daa7/download/" assertion.
- d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe contentUrl "https://w3id.org/ro-id/d5d3a3ed-7bc1-40b9-b2cd-0496f599d0fe" assertion.
- jmse10010112 contentUrl "https://doi.org/10.3390/jmse10010112" assertion.
- Two-line_element_set contentUrl "https://en.wikipedia.org/wiki/Two-line_element_set" assertion.
- master contentUrl "https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/" assertion.
- marivisu-v2 contentUrl "https://gitlab.com/simula_ais_message/marivisu-v2" assertion.
- pre-processing contentUrl "https://gitlab.com/simula_ais_message/pre-processing" assertion.
- vesseltype_identification_dae contentUrl "https://gitlab.com/simula_ais_message/vesseltype_identification_dae" assertion.
- apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf contentUrl "https://www.simula.no/sites/default/files/publications/files/apprentissage_auto_supervise_pour_detecter_les_deconnections_ais_volontaires.pdf" assertion.
- 88fba8bd-f2f0-402e-8147-b73b71e8691a creator 0000-0002-1784-2920 assertion.
- response-ais.html creator 0000-0002-1784-2920 assertion.
- tle-fmt.php creator 0000-0002-1784-2920 assertion.
- edit?usp=sharing creator 0000-0002-1784-2920 assertion.
- view?usp=sharing creator 0000-0002-1784-2920 assertion.
- view?usp=sharing creator 0000-0002-1784-2920 assertion.
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- 0AI6umItIl7BxUk9PVA creator 0000-0002-1784-2920 assertion.
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- ed59cb0a-e359-4f95-932d-88375b08daa7 creator 0000-0002-1784-2920 assertion.
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- marivisu-v2 creator 0000-0002-1784-2920 assertion.
- pre-processing creator 0000-0002-1784-2920 assertion.