Anomaly detection of ship behavior based on deep neural networks

•A HybridAttn-BiRNN model with temporal and feature attention is proposed for ship trajectory prediction.•An anomaly detection framework integrating HDBSCAN clustering and the HybridAttn-BiRNN model is established.•Multiple anomaly types are detected using threshold discrimination and feature deviat...

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Published in:	Reliability engineering & system safety Vol. 266; p. 111801
Main Authors:	Shu, Yaqing, Dong, Ao, Liu, Chengyong, Gan, Langxiong, Song, Lan
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.02.2026
Subjects:	AIS data Anomaly detection Deep neural networks HDBSCAN Ship behavior HDBSCAN AIS data Deep neural networks Anomaly detection Ship behavior
ISSN:	0951-8320
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Abstract	•A HybridAttn-BiRNN model with temporal and feature attention is proposed for ship trajectory prediction.•An anomaly detection framework integrating HDBSCAN clustering and the HybridAttn-BiRNN model is established.•Multiple anomaly types are detected using threshold discrimination and feature deviation analysis.•Ship anomalies are detected with 92.7 % accuracy using Shanghai Port AIS data in experiments. With the acceleration of the digitalization process in the global shipping industry. The anomaly detection of ship behaviors, including ship speed, course, and trajectory, has become a core challenge to ensure maritime traffic safety. To address this challenge, a method that integrates a hierarchical density-based spatial clustering algorithm (HDBSCAN) with a deep learning model is proposed in this paper. Unlike conventional approaches that rely solely on clustering or prediction models, AIS trajectories are compressed and clustered by our method to extract normal behavior patterns. Ship behavior is then predicted using a deep learning model with feature and temporal attention mechanisms. A dual-threshold mechanism is employed to identify various anomalies, including speed and course deviations, based on prediction errors. A case study is conducted using the AIS data from the waters of Shanghai Port. The experimental results show that abnormal ship behavior can be effectively identified by this method, with an average detection accuracy of 92.7% for various types of anomalies. The method proposed in this research could proactively identify potential maritime risks, thereby improving the overall safety and reliability of shipping operations.
AbstractList	•A HybridAttn-BiRNN model with temporal and feature attention is proposed for ship trajectory prediction.•An anomaly detection framework integrating HDBSCAN clustering and the HybridAttn-BiRNN model is established.•Multiple anomaly types are detected using threshold discrimination and feature deviation analysis.•Ship anomalies are detected with 92.7 % accuracy using Shanghai Port AIS data in experiments. With the acceleration of the digitalization process in the global shipping industry. The anomaly detection of ship behaviors, including ship speed, course, and trajectory, has become a core challenge to ensure maritime traffic safety. To address this challenge, a method that integrates a hierarchical density-based spatial clustering algorithm (HDBSCAN) with a deep learning model is proposed in this paper. Unlike conventional approaches that rely solely on clustering or prediction models, AIS trajectories are compressed and clustered by our method to extract normal behavior patterns. Ship behavior is then predicted using a deep learning model with feature and temporal attention mechanisms. A dual-threshold mechanism is employed to identify various anomalies, including speed and course deviations, based on prediction errors. A case study is conducted using the AIS data from the waters of Shanghai Port. The experimental results show that abnormal ship behavior can be effectively identified by this method, with an average detection accuracy of 92.7% for various types of anomalies. The method proposed in this research could proactively identify potential maritime risks, thereby improving the overall safety and reliability of shipping operations.
ArticleNumber	111801
Author	Liu, Chengyong Gan, Langxiong Shu, Yaqing Dong, Ao Song, Lan
Author_xml	– sequence: 1 givenname: Yaqing surname: Shu fullname: Shu, Yaqing organization: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China – sequence: 2 givenname: Ao surname: Dong fullname: Dong, Ao organization: School of Navigation, Wuhan University of Technology, Wuhan 430063, China – sequence: 3 givenname: Chengyong surname: Liu fullname: Liu, Chengyong organization: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China – sequence: 4 givenname: Langxiong surname: Gan fullname: Gan, Langxiong organization: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China – sequence: 5 givenname: Lan surname: Song fullname: Song, Lan email: lansong@eitech.edu.cn organization: College of Engineering, Eastern Institute of Technology, Ningbo 315200, China
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Keywords	HDBSCAN AIS data Deep neural networks Anomaly detection Ship behavior
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Snippet	•A HybridAttn-BiRNN model with temporal and feature attention is proposed for ship trajectory prediction.•An anomaly detection framework integrating HDBSCAN...
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SubjectTerms	AIS data Anomaly detection Deep neural networks HDBSCAN Ship behavior
Title	Anomaly detection of ship behavior based on deep neural networks
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