Ship Anomalous Behavior Detection Using Clustering and Deep Recurrent Neural Network

Author:

Zhang Bohan1,Hirayama Katsutoshi1,Ren Hongxiang2,Wang Delong3,Li Haijiang4

Affiliation:

1. Graduate School of Maritime Sciences, Kobe University, Kobe 658-0022, Japan

2. Key Laboratory of Marine Dynamic Simulation, Dalian Maritime University, Dalian 116026, China

3. Navigation College, Dalian Maritime University, Dalian 116026, China

4. School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China

Abstract

In this study, we propose a real-time ship anomaly detection method driven by Automatic Identification System (AIS) data. The method uses ship trajectory clustering classes as a normal model and a deep learning algorithm as an anomaly detection tool. The method is divided into three main steps: (1) quality maintenance of the original AIS data, (2) extraction of normal ship trajectory clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), in which a segmented improved Dynamic Time Warping (DTW) algorithm is used to measure the degree of trajectory similarity, (3) the clustering results are used as a normative model to train a Bi-directional Gated Recurrent Unit (BiGRU) recurrent neural network, which is used as a trajectory predictor to achieve real-time ship anomaly detection. Experiments were conducted using real AIS data from the port of Tianjin, China. The experimental results are manifold. Firstly, the data pre-processing process effectively improves the quality of raw AIS data. Secondly, the ship trajectory clustering model can accurately classify the traffic flow of different modes in the sea area. Moreover, the trajectory prediction result of the BiGRU model has the smallest error with the actual ship trajectory and has a better trajectory prediction performance compared with the Long Short-Term Memory Network model (LSTM) and Gated Recurrent Unit (GRU). In the final anomaly detection experiment, the detection accuracy and timeliness of the BiGRU model are also higher than LSTM and GRU. Therefore, the proposed method can achieve effective and timely detection of ship anomalous behaviors in terms of position, heading and speed during ship navigation, which provides insight to enhance the intelligence of marine traffic supervision and improve the safety of marine navigation.

Funder

China Scholarship Council

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

Reference30 articles.

1. International Maritime Organization (IMO) (1998). Resolution MSC. 74 (69): Adoption of New and Amended Performance Standards, International Maritime Organization (IMO).

2. Study of automatic anomalous behavior detection techniques for maritime vessels;Shu;J. Navig.,2017

3. Laxhammar, R. (July, January 30). Anomaly detection for sea surveillance. Information Fusion. Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany.

4. Data integrity assessment for maritime anomaly detection;Iphar;Expert Syst. Appl.,2020

5. Martineau, E., and Roy, J. (2011). Maritime Anomaly detection: Domain Introduction and Review of Selected Literature, Defence Research and Development Canada. Defense Research and Development Canada—Valcartier, Technical Memorandum TM2010-460.

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