The abuse of online games by automated programs, known as bots, has grown significantly in recent years. The conventional methods for distinguishing bots from humans, such as CAPTCHAs, are not effective in a gaming context. This talk presents a non-interactive approach based on human observational proofs for continuous game bot detection. HOPs differentiate bots from human players by passively monitoring input actions that are difficult for current bots to perform in a human-like manner. The talk describes a prototype HOP-based game bot defense system that analyzes user-input actions with a cascade-correlation neural network to distinguish bots from humans. The experimental results show that the HOP system is effective in capturing game bots in World of Warcraft, raising the bar against game exploits and forcing attackers to build more complicated bots for detection evasion in the future.