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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23619 |
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| _version_ | 1866911235100901376 |
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| author | Miao, Yuyang Xing, Huijun Mandic, Danilo P. Constantinides, Tony G. |
| author_facet | Miao, Yuyang Xing, Huijun Mandic, Danilo P. Constantinides, Tony G. |
| contents | This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduces an A/B/C/D station classification system that successfully identifies suspicious patterns across 30 high-risk stations. The framework employs four complementary algorithms: Isolation Forest, Local Outlier Factor, One-Class SVM, and Mahalanobis Distance. Key findings include the identification of five distinct short ticketing patterns and potential for short ticketing recovery in transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Short Ticketing Detection Framework Analysis Report Miao, Yuyang Xing, Huijun Mandic, Danilo P. Constantinides, Tony G. Cryptography and Security Artificial Intelligence This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduces an A/B/C/D station classification system that successfully identifies suspicious patterns across 30 high-risk stations. The framework employs four complementary algorithms: Isolation Forest, Local Outlier Factor, One-Class SVM, and Mahalanobis Distance. Key findings include the identification of five distinct short ticketing patterns and potential for short ticketing recovery in transportation systems. |
| title | Short Ticketing Detection Framework Analysis Report |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23619 |