Trust management for cheating detection in distributed massively multiplayer online games
James E. Prather, Robert Nix, Ryan K. Jessup · 2017
In this paper we examine the use of trust metrics for punishing dishonest (cheating) and malicious (griefing) behavior in peer-to-peer massively multiplayer online games (MMOG's). In particular, we first replicate the work of Goodman and Verbrugge and then propose a metric of our own. Our approach uses a reinforcement learning trust metric that can more rapidly and accurately detect dishonest and malicious behavior. We use a genetic algorithm which keeps in mind the goals of the system to select the parameters for our trust metric. Our metric produces a system which removes dishonest and malicious players from the system much faster than the original while nonetheless retaining a low false positive rate. The metric also produces a surprising result in which cheating and griefing are treated equally, despite being unequal offenses.