Abstract
Cheating detection in online gaming is a crucial challenge that affects the fairness and integrity of virtual environments. This literature review delves into the advancements made in the field of cheating detection, focusing on machine-learning-based approaches and encrypted network traffic analysis. Various methodologies, including Support Vector Machines, Logistic Regression, and GPU acceleration, are explored in detecting cheating behaviors within different gaming scenarios. The review also examines the application of supervised learning techniques in Unreal Tournament III, showcasing their potential in identifying cheating instances. Additionally, the challenges posed by limited labeled data and covariate shifts in encrypted network traffic analysis are addressed through innovative solutions like the GCI framework. Insights into the interplay of data attributes and classification performance are provided, offering directions for future research. Overall, this review contributes to the understanding of cheating detection strategies and their implications for maintaining equitable and enjoyable online gaming experiences.
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