Who ruins the game?: unveiling cheating players in the "Battlefield" game
Dong Young Kim, Huy Kang Kim
TL;DR
This study tackles cheating in Battlefield by applying lightweight statistical analysis to kill-log data collected from public reporting channels, avoiding in-game or server-side instrumentation. It demonstrates that cheating behavior is repeatable (hack_score closely tracks hack_score_current) and that certain indicators, such as unreleased_weapons_kills, strongly signal cheating across all rank levels. The approach relies on basic descriptive statistics, correlation analysis, and visualization to yield actionable insights that can inform real-time monitoring and prevention strategies without imposing heavy resource burdens. The findings highlight practical, data-driven cues for detecting cheaters and emphasize the need for continuous surveillance across all player tiers to preserve fair play in FPS environments.
Abstract
The "Battlefield" online game is well-known for its large-scale multiplayer capabilities and unique gaming features, including various vehicle controls. However, these features make the game a major target for cheating, significantly detracting from the gaming experience. This study analyzes user behavior in cheating play in the popular online game, the "Battlefield", using statistical methods. We aim to provide comprehensive insights into cheating players through an extensive analysis of over 44,000 reported cheating incidents collected via the "Game-tools API". Our methodology includes detailed statistical analyses such as calculating basic statistics of key variables, correlation analysis, and visualizations using histograms, box plots, and scatter plots. Our findings emphasize the importance of adaptive, data-driven approaches to prevent cheating plays in online games.