Collusion Detection in Team-Based Multiplayer Games

TL;DR

This paper presents a graph-theoretic, unsupervised learning approach to detect collusion in team-based multiplayer games, enabling game designers to identify suspicious player pairs efficiently while minimizing false positives.

Contribution

It introduces a novel method combining social relationship analysis, behavioral patterns, and Isolation Forest to detect collusion in large-scale multiplayer datasets.

Findings

Effective detection of colluding players in large datasets

High performance on real-world datasets with over 170,000 players

Efficient identification of suspicious player pairs

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.