Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data

Kontopoulos, Ioannis; Spiliopoulos, Giannis; Zissis, Dimitrios; Chatzikokolakis, Konstantinos; Artikis, Alexander

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00139

Cited by 19 publications

(11 citation statements)

References 5 publications

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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%

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%

A Deep Learning Streaming Methodology for Trajectory Classification

Kontopoulos

Makris

Tserpes

2021

IJGI

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“…In this work, we showcased a few real world examples which our model managed to accurately detect. Identifying specific cases of anomalous behavior [10,11,23,24] will allow us to fine-tune, improve and exploit the proposed unsupervised technique as a basis for a supervised model for the detection of events of interest in the maritime sector. As a future work, we intend to exploit the proposed network abstraction in order to identify events of interest to the maritime authorities.…”

Section: Conclusion and Future Stepsmentioning

confidence: 99%

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Kontopoulos

Varlamis

Tserpes

2020

Lecture Notes in Computer Science

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The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance. AIS transponders are rich sources of information that everyone can collect using an RF receiver and provide real-time information about vessels' position. Properly taking advantage of AIS data, can uncover potential illegal behavior, offer real-time alerts and notify the authorities for any kind of anomalous vessel behavior. In this article, we extend an existing network abstraction of maritime traffic, that is based on nodes (called way-points) that correspond to naval areas of long stays or major turns for vessels (e.g. ports, capes, offshore platforms etc.) and edges (called traversals) that correspond to the routes followed by vessels between two consecutive way-points. The current work, focuses on the connections of this network abstraction and enriches them with semantic information about the different ways that vessels employ when traversing an edge. For achieving this, it proposes an alternative of the popular density based clustering algorithm DB-Scan, which modifies the proximity parameter (i.e. epsilon) of the algorithm. The proposed alternative employs in tandem the difference in (i) speed, (ii) course and (iii) position for defining the distance between two consecutive vessel positions (two consecutive AIS signals received from the same vessel). The results show that this combination performs significantly better than using only the spatial distance and, more importantly, results in clusters that have very interesting properties. The enriched network model can be processed and further examined with data mining techniques, even in an unsupervised manner, in order to identify anomalies in vessels' trajectories. Experimental results on a real dataset show the network's potential for detecting trajectory outliers and uncovering deviations on a vessel's route.

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“…The authors of [13] mainly solved the problem of position spoofing by comparing the estimated AIS emitter position and position reported in AIS data, but it required related domain knowledge. The authors of [14] calculated the speed based on the distance and duration between two AIS points, and determined whether a spoofing event occurred based on the speed, this method was suitable for resolving spoofing events with large changes in position. However, there is less research on MMSI spoofing and AIS closing detection, and it is easier for ships to perform these two behaviors in illegal activities.…”

Section: Introductionmentioning

confidence: 99%

Detection of AIS Closing Behavior and MMSI Spoofing Behavior of Ships Based on Spatiotemporal Data

et al. 2020

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In marine transportation, many ships are equipped with AIS devices. The AIS data sent by AIS devices can help the maritime authorities and other ships obtain the navigation condition of the ship, thereby ensuring the safety of ships during navigation. However, when a ship is involved in illegal activities, the crew may close the AIS device or tamper with Maritime Mobile Service Identity (MMSI) in the AIS data. To detect these two kinds of behaviors, this paper designs the AIS closing detection algorithm and MMSI spoofing detection algorithm based on the spatiotemporal data provided by AIS and radar. As the radar data does not include the ship’s identification, the associated relationship between radar data and AIS data is difficult to determine in the multi-ship scenario. To solve this problem, the D-TRAP is defined in this paper, it is applied in the process of searching for the associated AIS points of radar trajectory points, when the number of effective AIS points is reduced caused by the above two behaviors, the association method used in the paper has better performance. In addition, real data and simulation data are used to verify the two algorithms. The verification results show that when the ship is simultaneously monitored by radar and AIS, and the monitoring process continues for a period of time, the AIS closing detection algorithm has good performance. When the ship is monitored by AIS and radar before and after MMSI spoofing, and both monitoring processes continue for a period of time, the MMSI spoofing algorithm has good performance.

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Countering Real-Time Stream Poisoning: An Architecture for Detecting Vessel Spoofing in Streams of AIS Data

Cited by 19 publications

References 5 publications

A Deep Learning Streaming Methodology for Trajectory Classification

A Deep Learning Streaming Methodology for Trajectory Classification

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Detection of AIS Closing Behavior and MMSI Spoofing Behavior of Ships Based on Spatiotemporal Data

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