During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government 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 fraudulent 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 validate 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 research, 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.
Despite the extensive ramifications of illegal online gambling (IOG) services, actions taken by government authorities have had little effect in halting these operations. In order to reduce the prevalence of IOG, the ability to detect malicious uniform resource locators (URLs) is crucial. Text mining and binary classification have been widely adopted to detect and prevent spam short message services (SMSs), but government authorities and various task forces that monitor and regulate gambling also rely on the analysis of malicious URLs. This study proposes a novel system to analyse the characteristics of spam URLs, offering a method that can assist government agencies combatting mobile IOG sites.
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