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The HOP system is effective in capturing current game bots, which raises the bar against game exploits and forces a determined adversary to build more complicated game bots for detection evasion in the future. Then, we develop a HOP-based game bot defense system that analyzes user-input actions with a cascade-correlation neural network to distinguish bots from humans. Based on the traces, we characterize the game playing behaviors of bots and humans. We collect a series of user-input traces in one of the most popular online games, World of Warcraft. HOPs differentiate bots from human players by passively monitoring input actions that are difficult for current bots to perform in a human-like manner. In this paper, we propose a non-interactive approach based on human observational proofs (HOPs) for continuous game bot detection. In particular, they are too obtrusive to be tolerated by human players in a gaming context. However, HIP-based approaches have inherent drawbacks. The current methods for distinguishing bots and humans are based on human interactive proofs (HIPs), such as CAPTCHAs. The abuse of online games by automated programs, known as game bots, for gaining unfair advantages has plagued millions of participating players with escalating severity in recent years.

Pirox bots