Detecting fraud in online games of chance and lotteries

Christou, Ioannis T.; Bakopoulos, Menelaos; Dimitriou, Tassos; Amolochitis, Emmanouil; Tsekeridou, Sofia; Dimitriadis, Christos K.

doi:10.1016/j.eswa.2011.04.124

Cited by 13 publications

(10 citation statements)

References 21 publications

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“…A new brand of frauds appear in the online gaming and lotteries, i.e. intended for money laundering, whose detection is dealt with a mixture of supervised and unsupervised classifiers [204]. To be adaptive to fraudster evolving strategies, it is required to emphasize online learning, and online cluster detection.…”

Section: New Fraud Trendsmentioning

confidence: 99%

A survey of multiple classifier systems as hybrid systems

2014

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A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-todate survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed.

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Section: New Fraud Trendsmentioning

confidence: 99%

A survey of multiple classifier systems as hybrid systems

2014

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“…Design and modelling of detection systems have been examined in the past (Islam et al, 2010;Skatteverket, 2008;Lundin et al, 2002;Christou et al, 2011;Srivastava et al, 2008), though this work has primarily focused on misuse detection and signature matching to ICS 25,4 detect fraud, cheating and intrusion in real-world situations. These issues, with relation to VWEs, have not been a focus of past or current research.…”

Section: Related Workmentioning

confidence: 99%

“…The shortcomings of this study aside from not being related to VWEs specifically, used just one fraud case study to test the model, which does not give a critical view of its success; more cases are required to prove its success. Christou et al (2011) presented a hybrid system designed to detect fraud in popular online games and lotteries, where winning or losing depends purely on chance. In these environments, fraud is quite common in the forms of money laundering and insider attack scenarios.…”

Section: Related Workmentioning

confidence: 99%

A cyber-threat analytic model for autonomous detection of virtual property theft

Patterson

Hobbs

Zhu

2017

ICS

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Purpose The purpose of this study is to provide a framework to detect and prevent virtual property theft in virtual world environments. The issue of virtual property theft is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property, only to have them stolen by thieves. Many virtual property thefts go undetected and often only discovered after the incident has occurred. Design/methodology/approach This paper presents the design of an autonomic detection framework to identify virtual property theft at two key stages: account intrusion and virtual property trades. Account intrusion is an unauthorized user attempting to gain access to an account and unauthorized virtual property trades are trading of items between two users which exhibit theft characteristics. Findings Initial tests of this framework on a synthetic data set show an 80 per cent detection rate. This framework allows virtual world developers to tailor and extend it to suit their specific requirements. It provides an effective way of detecting virtual property theft while being low maintenance, user friendly and cost effective. Originality/value To the author’s knowledge, there is no detection framework, system or tool that works on virtual property theft detection in virtual world environments without access to authentic virtual world data or attack data (because of privacy issues and unwillingness of virtual world environments companies to collaborate). The topic of virtual property theft, lack of existing labelled data sets, user anonymity, size of virtual world environments data sets and privacy issues with virtual world companies and a number of other critical factors distinguish this paper from previous studies.

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“…Cao et al [ 6 ] also use a clustering algorithm to detect malicious accounts in online social networks. Christou et al [ 7 ] perform fraud detection for online games of chance using a clustering approach. Bolton et al [ 4 ] investigated an anomaly detection approach for Peer Group Analysis based on the t -statistic.…”

Section: Introductionmentioning

confidence: 99%

“…In the iGaming business, the most common types of cyber-fraud are money laundering [ 7 ], identity theft, and bonus abuse. In money laundering cases, criminals attempt to mask the legitimacy of their funds by depositing, wagering, and finally withdrawing a percentage of their account; thus, creating a complex money trail.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry

Farrugia¹,

Zerafa²,

Cini³

et al. 2021

SN COMPUT. SCI.

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This paper presents a real-time fully autonomous prescriptive solution for explainable cyber-fraud detection within the iGaming industry. We demonstrate how our solution facilitates the time-consuming task of player risk and fraud assessment through prescriptive analytics. Our tool leverages machine learning algorithms and advancements in the field of eXplainable AI to derive smarter predictions empowered by local interpretable explanations in real-time. Our best-performing pipeline was able to predict fraudulent behaviour with an average precision of 84.2% and an area under the receiver operating characteristics of 0.82 on our dataset. We also addressed the phenomenon of concept-drift and discussed our empirical and data-driven strategy for detecting and dealing with this problem. Finally, we cover how local interpretable explanations can help adopt a pro-active stance in fighting fraud.

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Detecting fraud in online games of chance and lotteries

Cited by 13 publications

References 21 publications

A survey of multiple classifier systems as hybrid systems

A survey of multiple classifier systems as hybrid systems

A cyber-threat analytic model for autonomous detection of virtual property theft

A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry

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