Research on Anomaly Detection of Ship Behavior Based on Data Mining
DOI:
https://doi.org/10.54691/yzaagq42Keywords:
Data Mining, Hive Data Warehouse, AIS Data, Analysis Of Abnormal Ship Behavior.Abstract
With the increase in the number of ships and the intensifying maritime traffic, maritime accidents such as ship collisions and grounding occur frequently, posing serious threats to people's lives and property. Therefore, timely detection and correction of abnormal ship behaviors are of great significance for improving navigation safety. The Automatic Identification System (AIS) can provide real-time information on ships' positions, speeds, headings, and more, offering a rich data resource for data mining. Through the analysis of these data, it is possible to deeply explore the navigation patterns and characteristics of abnormal behaviors of ships. This paper applies data mining technology to the detection of abnormal ship behaviors, which not only improves the accuracy and efficiency of detection but also provides maritime regulatory authorities with richer decision-making support.
Downloads
References
[1] Rong, Hao, A. P. Teixeira, and C. Guedes Soares. "Data mining approach to shipping route characterization and anomaly detection based on AIS data." Ocean Engineering 198 (2020): 106936.
[2] Filipiak, Dominik, et al. "Big data for anomaly detection in maritime surveillance: spatial AIS data analysis for tankers." Maritime Technical Journal 215.4 (2018): 5-28.
[3] Liu, Hongdan, et al. "Research on detection mechanism of vessel abnormal behavior based on immune genetic spectral clustering algorithm." Ocean Engineering 266 (2022): 113099.
[4] Ikotun, Abiodun M., et al. "K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data." Information Sciences 622 (2023): 178-210.
[5] El Bouchefry, Khadija, and Rafael S. de Souza. "Learning in big data: Introduction to machine learning." Knowledge discovery in big data from astronomy and earth observation. Elsevier, 2020. 225-249.
[6] Landi, Federico, et al. "Working memory connections for LSTM." Neural Networks 144 (2021): 334-341.
[7] Akbar, Mohd, et al. "Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach." Cluster Computing 27.3 (2024): 3683-3702.
[8] Emmanuel, Tlamelo, et al. "A survey on missing data in machine learning." Journal of Big data 8 (2021): 1-37.
[9] Guan, Meng, Yu Cao, and Xu Cheng. "Research on the recognition of multiple MMSI codes on a single vessel based on AIS datas." IEEE Access (2023).
[10] Rong, H., A. P. Teixeira, and C. Guedes Soares. "Ship collision avoidance behaviour recognition and analysis based on AIS data." Ocean Engineering 245 (2022): 110479.
[11] Ahmed, Mohiuddin, Raihan Seraj, and Syed Mohammed Shamsul Islam. "The k-means algorithm: A comprehensive survey and performance evaluation." Electronics 9.8 (2020): 1295.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Science and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
