User identification based on game-play activity patterns

TLDR: This paper proposes a new biometric for human identification based on users' game-play activities, and proposes the RET scheme, which is based on the KullbackLeibler divergence between idle time distributions, for user identification.

Abstract: Account hijacking is considered one of the most serious security problems in online games A hijacker normally takes away valuable virtual items from the stolen accounts, and trades those items for real money Even though account hijacking is not uncommon, there is currently no general solutions to determine whether an account has been hijacked The game company is not aware of a hijack unless it is reported by the victim However, it is usually too late---usually a hijacker already took away anything valuable when a user finds that his/her account has been stolenIn this paper, we propose a new biometric for human identification based on users' game-play activities Our main summary are two-fold: 1) we show that the idle time distribution is a representative feature of game players; 2) we propose the RET scheme, which is based on the KullbackLeibler divergence between idle time distributions, for user identification Our evaluations shows that the RET scheme achieves higher than 90% accuracy with a 20-minute detection time given a 200-minute history size