Abstract

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.

Advisor

Fox, Nathan

Department

Computer Science; Mathematics

Disciplines

Artificial Intelligence and Robotics

Keywords

Cheat Detection, Machine Learning, Deep Learning, Counter-Strike

Publication Date

2020

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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© Copyright 2020 Harry Dunham