Maritime anomaly detection: A review

Riveiro, Maria; Pallotta, Giuliana; Vespe, Michele

doi:10.1002/widm.1266

Cited by 108 publications

(66 citation statements)

References 111 publications

(190 reference statements)

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“…Trajectory-based techniques treat the entire traffic data sequence as a whole. Several research directions are analyzed in the literature related to the analysis of vessel trajectories: maritime traffic pattern mining, ship collision risk assessment [12], maritime anomaly detection [13,14,15], identification of the types of ships [16], and combating abalone poaching [17].…”

Section: Reviewmentioning

confidence: 99%

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Venskus

Treigys

Bernatavičienė

et al. 2019

Sensors

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The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

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Section: Reviewmentioning

confidence: 99%

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Venskus

Treigys

Bernatavičienė

et al. 2019

Sensors

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“…Both research fields are strongly connected, since they share a common approach. The first step is to extract typical vessel behavior by applying statistical methods and Big Data techniques to a set of historical AIS data [6,7]. Following this, the extracted typical vessel behavior is used to detect anomalous behavior automatically from a new set of AIS data [6].…”

Section: Related Workmentioning

confidence: 99%

“…The first step is to extract typical vessel behavior by applying statistical methods and Big Data techniques to a set of historical AIS data [6,7]. Following this, the extracted typical vessel behavior is used to detect anomalous behavior automatically from a new set of AIS data [6]. For the purpose of behavior prediction, the typical vessel behavior is used to predict the future track [7].…”

Section: Related Workmentioning

confidence: 99%

Context-Sensitive Prediction of Vessel Behavior

Steidel

Mentjes

Hahn

2020

JMSE

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Research in the field of maritime anomaly detection and vessel behavior prediction primarily focuses on developing methods for extracting typical vessel movement patterns from historical traffic data. However, contextual information is currently not considered during pattern extraction by existing research. Combining contextual information with historical traffic data has the potential to produce both more accurate traffic patterns and more precise predictions of vessel behavior. This paper investigates the benefit of incorporating contextual information during the extraction of vessel behavior and the prediction of the most probable vessel behavior. A method is presented that combines historical vessel traffic data with information about the course of waterways. Typical behavior patterns are extracted by applying kernel density estimation, which are subsequently used for predicting the most probable vessel behavior. Using this approach, we were able to predict in which area the vessel is most likely to sail, as well as the actual track for a sailing time of 2:35 h. Additional potential applications of our approach can be derived from the results, which, in addition to behavior prediction, can also be used to detect anomalous vessel behavior.

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“…From a more practical perspective instead, operators manually search and try to predict critical situations, such as potential collisions and suspicious activities performed by many vessels within vast sea areas. In order to provide support in these operations, a number of methods and systems with anomaly detection capabilities have been proposed [4]. Indeed, over the past few years, anomaly detection strategies have been applied to maritime traffic monitoring [2], [3], [5]- [9], to detect, e.g., unexpected stops or course changes (path deviations), and, more generally, any vessel's anomalous behavior that might be related to potential suspicious activity.…”

Section: Introductionmentioning

confidence: 99%

Optimal Stealth Trajectory Design to Deceive Anomaly Detection Process

d'Afflisio

Aubry

Braca

et al. 2019

OCEANS 2019 - Marseille

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A new methodology is proposed to deceive an anomalous trajectory detector by designing ship paths that deviate from the nominal traffic routes in an optimized way. The route planning is formalized as a min-max problem (with respect to surveillance system acquisition instants) focusing on the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. Modeling the vessel's dynamic according to the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process, physical, practical, and kinematic requirements are also accounted for forcing several constraints at the design stage. A computationally efficient technique is proposed to handle the resulting non-convex optimization problem, and some case studies are reported to assess its effectiveness.

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Maritime anomaly detection: A review

Cited by 108 publications

References 111 publications

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Context-Sensitive Prediction of Vessel Behavior

Optimal Stealth Trajectory Design to Deceive Anomaly Detection Process

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