VTexplorer API (https://www.vtexplorer.com) documentation. This documentation can be useful to understand how AIS data can be processed.
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).
Main document (Google doc) provided when willing to start with TSAR Overview.
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.
Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches
Internally shared google drive with data and documents for T-SAR project
Slides (private) presenting the T-SAR project.
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.
Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
Description of Two-Line Element Set (TLE) from Wikipedia.
Gitlab repository set up for reproducibility purposes.
repo set up for reproducibility purposes (private gitlab)
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).
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.
Private repository containing Anomaly Detection in Vessels Trajectories using Context-Aware Autoencoders
vesseltype_identification_dae (gitlab private repo)
Here we gather information about the project (notes taken during meetings, etc.). We use hackmd.io and text is written in markdown.
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.
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.
papers, conference proceeding generated by the TSAR project.
Documentation and existing information about surveillance and detection of anomalies using Automatic Identification System data (ground and satellite).
Bibliography collected on Automatic Identification System and detection of anomalies from AIS data (ground/satellite).
Distribution of samples on the surface of the globe.
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).
NorSat-3: Ship Surveillance with a Navigation Radar Detector
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
Improved Testing of AI-Based Anomaly DetectionSystems Using Synthetic Surveillance Data
Research Object with sample AIS data (in-situ)
Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance
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).
AIS data prepared by Statsat AS for 2020
Politics/Political process/Political parties and movements
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.
Economy, business and finance/Economic sector/Transport/Waterway and maritime transport
Disaster, accident and emergency incident/Accident and emergency incident/Transport accident and incident/Air and space accident and incident
Science and technology/Social sciences/Linguistics
Society/Social problem/Juvenile delinquency
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.
In FFI proposed to deploy a microsatellite that could detect the navigational radar sig nals a Navigation Radar Detector (NRD). Now the Norwegian Space Centre has offered to include an experimental NRD on the NorSat microsatellite which is due for launch during spring .
Crime, law and justice/Crime/Computer crime
NorSat will be able to observe globally with both the AIS receiver and the radar detector, but the radar detector will mainly be used in the northern areas.
The yellow and orange symbols are AIS observations from the satellite which came in addition to the existing AIS observations (green and blue) from the Coastal Administration ground based sensors.
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.
Economy, business and finance/Economic sector/Computing and information technology/Satellite technology
Politics/Government/Defence/Military equipment
Economy, business and finance/Economic sector/Transport
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.
Self-supervised learning for the detection of illegal actions during maritime traffic monitoring