Show simple item record

dc.contributor.advisorWilliams, Daviden
dc.contributor.authorDsilva, Warren
dc.date.accessioned2021-04-28T17:45:19Z
dc.date.available2021-04-28T17:45:19Z
dc.date.issued2020
dc.identifier.citationDsilva, W. (2020). Game bot detection using behavioral analysis. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4223
dc.description.abstractOnline games and especially MMORPGs have become very popular and people are investing and spending thousands of dollars on such games. Enhancing the user-experience has become a challenge for companies as gaming bots have begun to populate most of the games. In this paper, we battle this problem by using behavioural analysis of players to detect and differentiate between bots and human players. Feature selection was a key factor in this process as choosing the right features enabled to get higher accuracy rates. Feature selection techniques such as Rank importance were used to select the most important features to train the data. The data was then fit using classification algorithms such as Naïve Bayes, Random Forest, Generalized Linear Model and Ensemble technique. The results indicate that the Random Forest algorithm performs the best with an accuracy of 96%en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectMachine learningen
dc.titleGame bot detection using behavioral analysisen
dc.typeThesisen
dc.rights.holderCopyright: The publisheren
dc.type.degreenameMSc in Data Analyticsen
dc.type.degreelevelMScen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record