Detecting anomalous transactions via an iot based application: A machine learning approach for horse racing betting

Moohong Min, Jemin Justin Lee, Hyunbeom Park, Kyungho Lee

Research output: Contribution to journalArticlepeer-review

Abstract

During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Govern-ment agencies and local authorities have place strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraud-ulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to vali-date the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our re-search, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

Original languageEnglish
Article number2039
Pages (from-to)1-17
Number of pages17
JournalSensors
Volume21
Issue number6
DOIs
Publication statusPublished - 2021 Mar 2

Keywords

  • Anomaly detection
  • Big data
  • Cyber security
  • Horse racing
  • Internet of Things
  • Machine learning
  • Mobile sensors
  • Time series data
  • Transaction data

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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