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This documentation can be useful to understand how AIS data can be processed." } ], "http://schema.org/encodingFormat" : [ { "@value" : "text/html" } ], "http://schema.org/keywords" : [ { "@value" : "API" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "VTexplorer API documentation." } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-08 11:55:50.363662+00:00" } ] }, { "@id" : "https://celestrak.org/NORAD/documentation/tle-fmt.php", "@type" : [ "http://purl.org/wf4ever/wf4ever#Document", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://celestrak.org/NORAD/documentation/tle-fmt.php" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-12-24 09:43:33.106536+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-24 09:43:33.827515+00:00" } ], "http://schema.org/description" : [ { "@value" : "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)." } ], "http://schema.org/keywords" : [ { "@value" : "TLE" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "NORAD Two-Line Element Set Format" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-24 09:43:33.106536+00:00" } ] }, { "@id" : "https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing", "@type" : [ "http://purl.org/wf4ever/wf4ever#Document", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://docs.google.com/document/d/19Qc0lhSPTjIbaZdruwgNjxIk-EW3C7HQ2BGX1iEKfRo/edit?usp=sharing" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-12-03 12:22:11.080763+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-03 12:22:11.948165+00:00" } ], "http://schema.org/description" : [ { "@value" : "Main document (Google doc) provided when willing to start with TSAR Overview." } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "TSAR Overview" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-03 12:22:11.080763+00:00" } ] }, { "@id" : "https://doi.org/10.3390/jmse10010112", "@type" : [ "http://purl.org/wf4ever/roterms#Paper", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://doi.org/10.3390/jmse10010112" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-10-19 12:26:00.283770+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-19 18:00:27.575120+00:00" } ], "http://schema.org/description" : [ { "@value" : "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." } ], "http://schema.org/keywords" : [ { "@value" : "AIS" }, { "@value" : "anomaly detection" }, { "@value" : "automatic identification system" }, { "@value" : "maritime safety" }, { "@value" : "maritime security" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-10-19 12:26:00.283770+00:00" } ] }, { "@id" : "https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/Thing" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://drive.google.com/drive/u/1/folders/0AI6umItIl7BxUk9PVA" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-10-05 13:34:23.536054+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-13 15:27:48.034278+00:00" } ], "http://schema.org/description" : [ { "@value" : "Internally shared google drive with data and documents for T-SAR project" } ], "http://schema.org/keywords" : [ { "@value" : "folder" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "TSAR google drive project area" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-10-05 13:34:23.536054+00:00" } ] }, { "@id" : "https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/PresentationDigitalDocument" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://drive.google.com/file/d/1VVlNufS9EkMcbOuhGD0uIWOEiMOj-hrW/view?usp=sharing" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-12-19 17:55:01.781065+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-19 18:00:49.447899+00:00" } ], "http://schema.org/description" : [ { "@value" : "Slides (private) presenting the T-SAR project." } ], "http://schema.org/keywords" : [ { "@value" : "AIS" }, { "@value" : "Vessel" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "TSAR project overview" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-19 17:55:01.781065+00:00" } ] }, { "@id" : "https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing", "@type" : [ "http://purl.org/wf4ever/roterms#Paper", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://drive.google.com/file/d/1t8pG1JOW7uj5gIbgmkXxILgzx1hO8FJk/view?usp=sharing" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-12-19 19:08:04.054398+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-19 19:08:05.371245+00:00" } ], "http://schema.org/description" : [ { "@value" : "In maritime traffic surveillance, detecting illegal\nactivities, such as illegal fishing or transshipment of illicit\nproducts 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\ndue 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.\nUsing 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\nAIS 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." } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-19 19:08:04.054398+00:00" } ] }, { "@id" : "https://en.wikipedia.org/wiki/Two-line_element_set", "@type" : [ "http://purl.org/wf4ever/wf4ever#Document", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://en.wikipedia.org/wiki/Two-line_element_set" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-12-26 10:22:06.052178+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-12-26 10:22:07.502404+00:00" } ], "http://schema.org/description" : [ { "@value" : "Description of Two-Line Element Set (TLE) from Wikipedia." } ], "http://schema.org/keywords" : [ { "@value" : "wikipedia" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "Two-Line Element Set (wikipedia)" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-12-26 10:22:06.052178+00:00" } ] }, { "@id" : "https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#SWDocumentation" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://gitlab.com/reproducibility-code/context-aware-autoencoders-for-anomaly-detection-in-maritime-surveillance/-/tree/master/" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2023-01-05 14:54:54.005940+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2023-01-05 14:54:57.935510+00:00" } ], "http://schema.org/description" : [ { "@value" : "Gitlab repository set up for reproducibility purposes." } ], "http://schema.org/keywords" : [ { "@value" : "gitlab" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "repo set up for reproducibility purposes (private gitlab)" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2023-01-05 14:54:54.005940+00:00" } ] }, { "@id" : "https://gitlab.com/simula_ais_message/marivisu-v2", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://w3id.org/ro/earth-science#SWDocumentation" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://gitlab.com/simula_ais_message/marivisu-v2" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-10-05 13:37:31.278559+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-10-05 13:37:32.047690+00:00" } ], "http://schema.org/description" : [ { "@value" : "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)." } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "Marivisu v2" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-10-05 13:37:31.278559+00:00" } ] }, { "@id" : "https://gitlab.com/simula_ais_message/pre-processing", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://w3id.org/ro/earth-science#SWDocumentation" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://gitlab.com/simula_ais_message/pre-processing" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-10-05 13:38:39.041164+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-10-05 13:38:39.630828+00:00" } ], "http://schema.org/description" : [ { "@value" : "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." } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-10-05 13:38:39.041164+00:00" } ] }, { "@id" : "https://gitlab.com/simula_ais_message/vesseltype_identification_dae", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "https://w3id.org/ro/terms/earth-science#SWDocumentation" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://gitlab.com/simula_ais_message/vesseltype_identification_dae" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-11-15 13:19:46.330017+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2022-11-15 13:19:53.225356+00:00" } ], "http://schema.org/description" : [ { "@value" : "Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders" } ], "http://schema.org/license" : [ { "@id" : "https://choosealicense.com/no-permission/" } ], "http://schema.org/name" : [ { "@value" : "vesseltype_identification_dae (gitlab private repo)" } ], "http://schema.org/sdDatePublished" : [ { "@value" : "2022-11-15 13:19:46.330017+00:00" } ] }, { "@id" : "https://hackmd.io/@simula/tsar-project", "@type" : [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/HowTo", "http://schema.org/MediaObject" ], "http://schema.org/author" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentUrl" : [ { "@value" : "https://hackmd.io/@simula/tsar-project" } ], "http://schema.org/creator" : [ { "@id" : "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated" : [ { "@value" : "2022-10-06 08:13:34.706608+00:00" } ], "http://schema.org/dateModified" : [ { "@value" : "2023-01-18 15:58:48.133720+00:00" } ], "http://schema.org/description" : [ { "@value" : "Here we gather information about the project (notes taken during meetings, etc.). 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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. 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