Game playing tactic as a behavioral biometric for human identification

This chapter expends behavior based intrusion detection approach to a new domain of game networks. Specifically, our research shows that a behavioral biometric signature can be generated based on the strategy used by an individual to play a game. We wrote software capable of automatically extracting behavioral profiles for each player in a game of poker. Once a behavioral signature is generated for a player, it is continuously compared against player's current actions. Any significant deviations in behavior are reported to the game server administrator as potential security breaches. In this chapter, we report our experimental results with user verification and identification, as well as our approach to generation of synthetic poker data and potential spoofing approaches of the developed system. We also propose utilizing techniques developed for behavior based recognition of humans to the identification and verification of intelligent game bots. Our experimental results demonstrate feasibility of such methodology.