Real-time maritime anomaly detection: detecting intentional AIS switch-off

Kontopoulos, Ioannis; Chatzikokolakis, Konstantinos; Zissis, Dimitris; Tserpes, Konstantinos; Spiliopoulos, Giannis

doi:10.1504/ijbdi.2020.107375

Cited by 35 publications

(19 citation statements)

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“…On the other hand, trajectory clustering approaches are often employed to form groups of AIS positions with similar spatiotemporal behaviors, uncovering behaviors that are harder to predefine. Although there is an abundance of studies in the literature regarding offline trajectory classification and clustering [1][2][3][4][5], fewer works have focused on steam processing of events in the maritime domain [6][7][8][9][10]. Event processing methodologies are faced with significant challenges when employed on streaming data where the requirements for such applications demand low memory consumption and decreased latencies.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an image classification approach for trajectory classification yields a promising universal approach for the classification of mobility patterns; • Approximately 16,000 AIS messages are generated each second from 200,000 vessels worldwide, resulting in 46GB of data per day. In the maritime domain, only in recent years have researchers started tackling the problem of real-time stream processing with the use of AIS messages [6][7][8][9][10]. To the best of our knowledge, this is the first time in the maritime domain literature that computer vision techniques have been used in real time to classify trajectories.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Streaming Methodology for Trajectory Classification

Kontopoulos

Makris

Tserpes

2021

IJGI

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Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning Streaming Methodology for Trajectory Classification

Kontopoulos

Makris

Tserpes

2021

IJGI

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“…Amongst the potential behaviours that can denote some anomalies and then potential piracy acts that happen at sea let us mention an abnormal change of position, usurpation of identity (or AIS Spoofing) [10]. Some boats can also no longer transmit their positions during a given time intentionally (or Going Dark) [7] and are difficult to detect considering [12] signal loss for AIS data. In order to address all these issues in a timely manner, not only a sound analysis of AIS data should be provided as most of the above approaches do, but also integration of additional sensor-based capabilities provided by Unmanned Aerial Vehicle (UAVs), semi-autonomous or more conventional systems (radars, human observations etc.…”

Section: Cyber Risks At Seamentioning

confidence: 99%

ISOLA: An Innovative Approach to Cyber Threat Detection in Cruise Shipping

Laso

Salmon

Bozhilova

et al. 2021

Smart Innovation, Systems and Technologies

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“…Such gaps may affect the quality of the traffic patterns extracted with the proposed methodology, since they directly affect the quality of the clustering step that follows (see Section 3.4). It is quite common to have such gaps in vessel trajectories because although it is mandatory for vessels to carry an AIS transponder, it is not compulsory for the transponder to be switched on (Guerriero et al (2008), Mazzarella et al (2017), Kontopoulos et al (2020)). This is a common tactic when vessels want to hide their tracks and conceal their whereabouts, in order to avoid piracy attacks, or perform an illegal act themselves (e.g.…”

Section: Lagrange Interpolationmentioning

confidence: 99%

“…The new network abstraction aims to extract multiple movement patterns for the same network edge, which correspond to a fine-grained clustering of the collected AIS data. Using Lagrange interpolation for adding intermediate points to the vessel trajectories improves the performance of the clustering algorithm Kontopoulos et al (2020). The algorithm parameters are fine-tuned by considering vessels with similar properties (e.g.…”

Section: Introductionmentioning

confidence: 99%

A distributed framework for extracting maritime traffic patterns

Kontopoulos

Varlamis

Tserpes

2020

International Journal of Geographical Information Science

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All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels' trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

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Real-time maritime anomaly detection: detecting intentional AIS switch-off

Cited by 35 publications

References 0 publications

A Deep Learning Streaming Methodology for Trajectory Classification

A Deep Learning Streaming Methodology for Trajectory Classification

ISOLA: An Innovative Approach to Cyber Threat Detection in Cruise Shipping

A distributed framework for extracting maritime traffic patterns

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