Server-side Anti-cheat in FPS Games for Aimbot Detection using Deep Learning and Machine Learning

Jul 16, 2026

Upload by Violet

Modern multiplayer first-person shooter (FPS) games require reliable anti-cheat mechanisms as the use of aimbots, wallhacks, and speed hacks increases. This work targets aimbot detection only by contrasting normal players with cheaters who typically ignore other gameplay aspects. It combines time-series features (e.g., aim velocity, shots, and target distance) with behavioral signals (e.g., utility usage and movement patterns) to build a server-side classifier named YAACS. YAACS uses a Stacked LSTM with Dense layers over sequences of 128 ticks, achieving 88.6% accuracy and 0.97% false positives, emphasizing temporal modeling to reduce false accusations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

Abstract

Keywords

Introduction

Rationale for Anti-cheat in FPS Games

Overview of Server-side vs Client-side Detection

Focus on Aimbot Detection

arXiv :2607 .04336v 1 [ cs .AI ] 5 Jul 2026 Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning. Siddhesh A. Dhingee , Shubham G. Sukume , Harsh S. Ranjanee , Ruturajsingh R. Rajpute , Jyoti H. Jadhave adept. of Information Technology, Pune Institute of Computer Technology, Pune, India [dhinge.siddhesh22@gmail.com](dhinge.siddhesh22@gmail.com) b dept. of Information Technology, Pune Institute of Computer Technology, Pune, India [shubhamsukum1722002@gmail.com](shubhamsukum1722002@gmail.com) c dept. of Information Technology, Pune Institute of Computer Technology, Pune, India [harshranjane2411@gmail.com](harshranjane2411@gmail.com) d dept. of Information Technology, Pune Institute of Computer Technology, Pune, India [ruturajsinghrajput@gmail.com](ruturajsinghrajput@gmail.com) edept. of Information Technology, Pune Institute of Computer Technology, Pune, India [jhjadhav@pict.edu](jhjadhav@pict.edu) Abstract Modern video games are becoming more complex day by day. Most of these modern games are multiplayer first-person shooter (FPS) games. The rising popularity of FPS games emphasizes the need to combat cheating for fair and enjoyable gaming. As the number of players using cheating techniques like aimbots, wallhacks, and speed hacks is also increasing, we need a way to detect players who are using cheating tools to gain an unfair advantage over regular players. In this system, we focus exclusively on detecting aimbot cheats. Players who use aimbot cheats generally do not prioritize other aspects of the game. To distinguish between regular and cheating players, we identify specific features encompassing time-series data such as aim velocity, number of shots, distance to target, and more, along with behavioral data such as utility usage, player movement, and other gameplay patterns. Utilizing these features, we construct a server-side aimbot detection classifier named ‘YAACS’. YAACS comprises a parser, a deep learning model, and intermediary connection utilities designed for integration with the game server. The proposed system achieves a classification accuracy of 88 .6% with a false positive rate of 0.97% using a Stacked LSTM with Dense layers trained on sequences of 128 ticks (Tick Delta Negative=56, Tick Delta Positive=24), outperforming the Decision Tree baseline which achieves a higher accuracy of 96.2% but at a false positive rate of 2.68%, 2.76x worse than the best LSTM configuration. These results demonstrate that incorporating temporal context through sequence modelling is critical for minimising false accusations in FPS cheat detection. Keywords: First Person Shooter video game, Deep learning, Aimbot detection, Long Short-Term Memory (LSTM), Time Series Classification, Cheat Detection, Sequence Modelling, Anti-cheat Systems 1. Introduction Video games have grown immensely in popularity and profitability among today’s youngsters. Integrity and fair play are essential components of the gaming experience, especially in contemporary multiplayer games. Firstperson shooter (FPS) games are the genre in which the majority of these modern games belong. First-person shooter games are getting more and more popular, drawing thousands of gamers every day. At the forefront are games like Call of Duty: Modern Warfare, Valorant, Counter-Strike: Global Offensive, and many more. Balanced, skill-based encounters are essential for competitive and online first-person shooter games, allowing players to assess their skills on an even playing field. Authentic and fair gameplay becomes more difficult to maintain as online gaming communities and organizations grow. Modern first-person shooter games are more complicated than ever, which causes more issues for gamers and less equitable gameplay. This is frequently the result of specific groups getting hold of game codes and abusing the title and its assets. This is when it becomes clear that an anti-cheat system is e

FAQs

What is the main goal of the YAACS system?

YAACS is designed to detect players using aimbot cheats in multiplayer FPS games using server-side classification.

Which data types are used for classification?

The system uses time-series gameplay features such as aim velocity, number of shots, and distance to target, along with behavioral data like utility usage and player movement patterns.

How does the proposed model evaluate performance against a baseline?

Using a Stacked LSTM with Dense layers yields 88.6% accuracy with a 0.97% false positive rate, and it is positioned as outperforming the Decision Tree baseline that reaches higher accuracy but with a significantly higher false positive rate.

Jul 16, 2026

Upload by Violet