Detecting Cybercrime in Online Video Gaming

Cybercrime is often assumed to be limited to more mature economic sectors. Yet, cybercrime is known to migrate to less tightly regulated domains—including online video gaming. Account compromise and virtual asset theft is a challenge that confronts the entire online video gaming industry. Increasingly, video game companies are required to promptly identify malicious online activity and take prompt remedial action. This paper conducts a social network analysis of 358,054 Roblox users that participated in the Roblox virtual asset marketplace over a 12-month period. Results from a multiple logistic regression analysis provide video game companies with actionable findings that can be leveraged during the implementation of organizational security controls, including policy, governance mechanism and system design decisions. Key findings reveal that the prosocial nature of online gamers’ friendship circles play a central role in determining the likelihood that accounts are banned for malicious account activity. Third-party trading website usage, posting trade advertisements as part of a social engineering exploit, and the age of user accounts constitute further risk factors that should be accounted for when managing customer risk. To complement the regression analysis, five classifiers were trained with social network-derived features. Cross-validated results show that network-derived features have strong discriminative power and should form part of a defense-in-depth approach to combatting cybercrime in online video gaming.