Short Ticketing Detection Framework Analysis Report

📝 Original Info

• Title: Short Ticketing Detection Framework Analysis Report
• ArXiv ID: 2510.23619
• Date: 2025-10-21
• Authors: ** 제공된 원문에 저자 정보가 포함되어 있지 않습니다. **

📝 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.

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