Publication Date
2017
Document Type
Thesis
Committee Members
Tanvi Banerjee (Committee Member), Michelle Cheatham (Advisor), Mateen Rizki (Committee Member)
Degree Name
Master of Science (MS)
Abstract
The success of any multiplayer game depends on the player’s experience. Cheating/Hacking undermines the player’s experience and thus the success of that game. Cheaters, who use hacks, bots or trainers are ruining the gaming experience of a player and are making him leave the game. As the video game industry is a constantly increasing multibillion dollar economy, it is crucial to assure and maintain a state of security. Players reflect their gaming experience in one of the following places: multiplayer chat, game reviews, and social media. This thesis is an exploratory study where our goal is to experiment and propose a new way to detect, mitigate cheating in Massively Multiplayer Online Role Playing Games by performing a multiclass classification on these unstructured textual data to categorize cheaters and victims with good classification accuracy that is acceptable for practical applications. In this thesis, First, we have studied the current situation regarding cheating and anti-cheating in online games. Second, we have studied various Natural Language Processing and Machine learning methods and tools for text classification. Third, a general method for automatic player categorization is proposed and finally, its performance is evaluated by experimenting on various datasets.
Page Count
108
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2017
Copyright
Copyright 2017, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
