Ship Anomalous Behavior Detection Based on BPEF Mining and Text Similarity Article Swipe
Yongfeng Suo
,
Yongxian Wang
,
Lei Cui
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13020251
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmse13020251
Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining.
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- Type
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- en
- Landing Page
- https://doi.org/10.3390/jmse13020251
- OA Status
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- References
- 38
- Related Works
- 10
- OpenAlex ID
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https://doi.org/10.3390/jmse13020251Digital Object Identifier
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Ship Anomalous Behavior Detection Based on BPEF Mining and Text SimilarityWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-29Full publication date if available
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Yongfeng Suo, Yongxian Wang, Lei CuiList of authors in order
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https://doi.org/10.3390/jmse13020251Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/jmse13020251Direct OA link when available
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Similarity (geometry), Computer science, Data mining, Information retrieval, Artificial intelligence, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.anomaly | 56, 68 |
| abstract_inverted_index.between | 52, 127, 143 |
| abstract_inverted_index.capture | 49 |
| abstract_inverted_index.extract | 100 |
| abstract_inverted_index.feature | 43 |
| abstract_inverted_index.flagged | 160 |
| abstract_inverted_index.illegal | 24 |
| abstract_inverted_index.measure | 151 |
| abstract_inverted_index.methods | 29, 186 |
| abstract_inverted_index.mining. | 221 |
| abstract_inverted_index.overall | 141 |
| abstract_inverted_index.pattern | 82 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.through | 114, 217 |
| abstract_inverted_index.vessels | 154 |
| abstract_inverted_index.BPEF-TSD | 75, 168 |
| abstract_inverted_index.Finally, | 137 |
| abstract_inverted_index.HDBSCAN. | 192 |
| abstract_inverted_index.Maritime | 0 |
| abstract_inverted_index.abnormal | 176 |
| abstract_inverted_index.accuracy | 173 |
| abstract_inverted_index.achieves | 170 |
| abstract_inverted_index.behavior | 1, 81, 102, 111, 135, 144, 216 |
| abstract_inverted_index.complete | 109 |
| abstract_inverted_index.employed | 119 |
| abstract_inverted_index.ensuring | 10 |
| abstract_inverted_index.iForest, | 190 |
| abstract_inverted_index.insights | 197 |
| abstract_inverted_index.limiting | 55 |
| abstract_inverted_index.maritime | 6, 200, 218 |
| abstract_inverted_index.methods. | 90 |
| abstract_inverted_index.proposed | 64 |
| abstract_inverted_index.research | 210 |
| abstract_inverted_index.sequence | 112 |
| abstract_inverted_index.struggle | 47 |
| abstract_inverted_index.temporal | 115 |
| abstract_inverted_index.valuable | 196 |
| abstract_inverted_index.accuracy. | 58 |
| abstract_inverted_index.advancing | 203 |
| abstract_inverted_index.algorithm | 122 |
| abstract_inverted_index.alignment | 126 |
| abstract_inverted_index.analysis, | 45 |
| abstract_inverted_index.anomalous | 32, 133, 214 |
| abstract_inverted_index.behaviors | 34 |
| abstract_inverted_index.detecting | 31, 175, 213 |
| abstract_inverted_index.detection | 2, 69 |
| abstract_inverted_index.enhancing | 199 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.exceeding | 155 |
| abstract_inverted_index.framework | 98, 169 |
| abstract_inverted_index.generated | 108 |
| abstract_inverted_index.primarily | 35 |
| abstract_inverted_index.strategy, | 152 |
| abstract_inverted_index.threshold | 158 |
| abstract_inverted_index.activities | 25 |
| abstract_inverted_index.algorithm, | 84 |
| abstract_inverted_index.anomalous. | 162 |
| abstract_inverted_index.behaviors, | 54 |
| abstract_inverted_index.challenge, | 62 |
| abstract_inverted_index.framework, | 70 |
| abstract_inverted_index.framework. | 76 |
| abstract_inverted_index.integrates | 78 |
| abstract_inverted_index.introduced | 94 |
| abstract_inverted_index.predefined | 157 |
| abstract_inverted_index.prevention | 22 |
| abstract_inverted_index.sequences. | 136 |
| abstract_inverted_index.similarity | 88, 142, 150 |
| abstract_inverted_index.alternative | 185 |
| abstract_inverted_index.contributes | 195 |
| abstract_inverted_index.demonstrate | 165 |
| abstract_inverted_index.intelligent | 204 |
| abstract_inverted_index.introducing | 207 |
| abstract_inverted_index.management, | 9 |
| abstract_inverted_index.measurement | 89 |
| abstract_inverted_index.navigation, | 13 |
| abstract_inverted_index.operations, | 16 |
| abstract_inverted_index.perspective | 211 |
| abstract_inverted_index.protection, | 19 |
| abstract_inverted_index.recognition | 83 |
| abstract_inverted_index.supervision | 205 |
| abstract_inverted_index.surveillance | 7 |
| abstract_inverted_index.trajectories | 177 |
| abstract_inverted_index.Specifically, | 91 |
| abstract_inverted_index.combinations. | 116 |
| abstract_inverted_index.environmental | 18 |
| abstract_inverted_index.outperforming | 184 |
| abstract_inverted_index.relationships | 51 |
| abstract_inverted_index.identification | 57 |
| abstract_inverted_index.Simultaneously, | 117 |
| abstract_inverted_index.Smith–Waterman | 121 |
| abstract_inverted_index.Smith–Waterman, | 85 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.03541592 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |