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

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 can often only be discovered after the incident has occurred. 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 unauthorised user attempting to gain access to an account and unauthorised virtual property trades are trading of items between two users which exhibit theft characteristics. Initial tests of this framework on a synthetic data set show an 80% 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 a low maintenance, user friendly and cost effective.