Gaming Bot Detection: A Systematic Literature Review

Kotkov, Denis; Pandey, Gaurav; Semenov, Alexander

doi:10.1007/978-3-030-04648-4_21

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…This paper contributes to the body of knowledge by proposing a machine learning-based cheat detection framework tailored to Unreal Tournament III, aiming to enhance the effectiveness and adaptability of cheat detection in the gaming domain. [3]…”

Section: Related Workmentioning

confidence: 99%

Robust Vision-Based Cheat Detection in Competitive Gaming

2023

IRJMETS

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The explosive growth of competitive online gaming has brought with it a surge in cheating activities, threatening the integrity of e-sports and online gaming communities. In response to this challenge, this paper presents a novel approach for detecting cheats in competitive gaming environments, leveraging computer vision techniques and machine learning algorithms. Our developed system employs a multi-modal approach that combines video frame analysis, player behavior modeling, and in-game event monitoring. By analyzing the visual input of gameplay, the system identifies irregularities, such as aimbots, wall hacks, and other unfair advantages. Furthermore, it takes into account player behavior patterns, considering movements, interactions, and decision-making processes that deviate from expected norms. The in-game event monitoring component examines game logs and detects abnormal events or discrepancies that might indicate cheating. To train the cheat detection model, a vast dataset of gameplay footage and player actions from various gaming titles was collected and annotated. Our system utilizes Deep Neural Networks (DNN) and OpenCV, to process this data and identify suspicious activities with high accuracy. The proposed cheat detection system is designed to be robust and adaptable, capable of detecting cheats across multiple gaming platforms and titles. It has undergone extensive testing and validation in real-world competitive gaming environments, achieving remarkable results in terms of accuracy and false positive rates.

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Section: Related Workmentioning

confidence: 99%

Robust Vision-Based Cheat Detection in Competitive Gaming

2023

IRJMETS

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“…To prevent a privacy breach, some propose server-side cheat-detection based on game logs to find anomalies in-game events and performance metrics [Alayed et al 2013]. Given the large amount of data in the logs, machine learning techniques like decision trees, naive Bayes, random forests, DNNs, or support vector machines [Galli et al 2011][Kotkov et al 2018] are a good fit for cheat detection, but currently available anti-cheating systems mainly focus on non-visual features [Cox et al 2019;FACEIT 2021;Punkbuster 2000;Wells 2020] [Islam et al 2020. For example, Punkbuster [Punkbuster 2000] scans the memory of the player machine, HestiaNet [Wells 2020] analyzes data using machine learning, VACNet [Cox et al 2019] analyzes players actions and decisions.…”

Section: Prior Workmentioning

confidence: 99%

Robust Vision-Based Cheat Detection in Competitive Gaming

Jonnalagadda

Frosio

Schneider

et al. 2021

Proc. ACM Comput. Graph. Interact. Tech.

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Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the frame buffer's final state and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation (IBP) to build a detector that is also resistant to potential adversarial attacks and study IBP's interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.

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Deep learning applications in games: a survey from a data perspective

Hu,

Ding,

et al. 2023

Appl Intell

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Gaming Bot Detection: A Systematic Literature Review

Cited by 5 publications

References 21 publications

Robust Vision-Based Cheat Detection in Competitive Gaming

Robust Vision-Based Cheat Detection in Competitive Gaming

Robust Vision-Based Cheat Detection in Competitive Gaming

Deep learning applications in games: a survey from a data perspective

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