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Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data

Singh, Sandeep Kumar and Heymann, Frank (2020) Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data. IEEE. IEEE/ION Position Location and Navigation Symposium (PLANS), USA, Portland, OR, USA. doi: 10.1109/PLANS46316.2020.9109806. ISBN 978-1-7281-0244-3. ISSN 2153-3598.

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Official URL: https://ieeexplore.ieee.org/abstract/document/9109806

Abstract

The automatic identification system (AIS) reports vessels’ static and dynamic information, which are essential for maritime traffic situation awareness. However, AIS transponders can be switched off to hide suspicious activities, such as illegal fishing, or piracy. Therefore, this paper uses real world AIS data to analyze the possibility of successful detection of various anomalies in the maritime domain. We propose a multi-class artificial neural network (ANN)-based anomaly detection framework to classify intentional and non-intentional AIS on-off switching anomalies. The multi-class anomaly framework captures AIS message dropouts due to various reasons, e.g., channel effects or intentional one for carrying illegal activities. We extract position, speed, course and timing information from real world AIS data, and use them to train a 2-class (normal and anomaly) and a 3-class (normal, power outage and anomaly) anomaly detection models. Our results show that the models achieve around 99.9% overall accuracy, and are able to classify a test sample in the order of microseconds.

Item URL in elib:https://elib.dlr.de/135174/
Document Type:Conference or Workshop Item (Speech)
Title:Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Singh, Sandeep KumarGerman Aerospace Center (DLR)https://orcid.org/0000-0002-8734-9832
Heymann, FrankGerman Aerospace Center (DLR)UNSPECIFIED
Date:April 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/PLANS46316.2020.9109806
Page Range:pp. 832-838
Publisher:IEEE
ISSN:2153-3598
ISBN:978-1-7281-0244-3
Status:Published
Keywords:Maritime, anomaly detection, machine learning
Event Title:IEEE/ION Position Location and Navigation Symposium (PLANS), USA
Event Location:Portland, OR, USA
Event Type:international Conference
Organizer:Institute of Navigation (ION)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - I4Port
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Singh, Sandeep Kumar
Deposited On:16 Jul 2020 18:17
Last Modified:20 Jul 2020 16:54

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