{"ID":2852455,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23619","arxiv_id":"2510.23619","title":"Short Ticketing Detection Framework Analysis Report","abstract":"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.","short_abstract":"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 empl...","url_abs":"https://arxiv.org/abs/2510.23619","url_pdf":"https://arxiv.org/pdf/2510.23619v1","authors":"[\"Yuyang Miao\",\"Huijun Xing\",\"Danilo P. Mandic\",\"Tony G. Constantinides\"]","published":"2025-10-21T20:50:48Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\"]","methods":"[]","has_code":false}