Deep learning is becoming a steadfast means of solving complex problems that do not have a single concrete or simple solution. One complex problem that fits this description and that has also begun to appear at the forefront of society is cheating, specifically within video games. Therefore, this paper presents a means of developing a deep learning framework that successfully identifies cheaters within the video game CounterStrike: Global Offensive. This approach yields predictive accuracy metrics that range between 80-90% depending on the exact neural network architecture that is employed. This approach is easily scalable and applicable to all types of games due to this project's basic design philosophy and approach.
Computer Science; Mathematics
Dunham, Harry, "Cheat Detection using Machine Learning within Counter-Strike: Global Offensive" (2020). Senior Independent Study Theses. Paper 8948.
Artificial Intelligence and Robotics
Cheat Detection, Machine Learning, Deep Learning, Counter-Strike
Bachelor of Arts
Senior Independent Study Thesis
© Copyright 2020 Harry Dunham