Collusion Detection in Team-Based Multiplayer Games
- URL: http://arxiv.org/abs/2203.05121v1
- Date: Thu, 10 Mar 2022 02:37:39 GMT
- Title: Collusion Detection in Team-Based Multiplayer Games
- Authors: Laura Greige, Fernando De Mesentier Silva, Meredith Trotter, Chris
Lawrence, Peter Chin and Dilip Varadarajan
- Abstract summary: We propose a system that detects colluding behaviors in team-based multiplayer games.
The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns.
We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
- Score: 57.153233321515984
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the context of competitive multiplayer games, collusion happens when two
or more teams decide to collaborate towards a common goal, with the intention
of gaining an unfair advantage from this cooperation. The task of identifying
colluders from the player population is however infeasible to game designers
due to the sheer size of the player population. In this paper, we propose a
system that detects colluding behaviors in team-based multiplayer games and
highlights the players that most likely exhibit colluding behaviors. The game
designers then proceed to analyze a smaller subset of players and decide what
action to take. For this reason, it is important and necessary to be extremely
careful with false positives when automating the detection. The proposed method
analyzes the players' social relationships paired with their in-game behavioral
patterns and, using tools from graph theory, infers a feature set that allows
us to detect and measure the degree of collusion exhibited by each pair of
players from opposing teams. We then automate the detection using Isolation
Forest, an unsupervised learning technique specialized in highlighting
outliers, and show the performance and efficiency of our approach on two real
datasets, each with over 170,000 unique players and over 100,000 different
matches.
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