Aimbottien havaitseminen keinotekoisilla neuroverkoilla

FPS-peleissä aimbottien käyttäjillä on epäreilu etu suhteessa rehellisiin pelaajiin, mutta huijausohjelmien käytön havaitseminen perinteisin keinoin on jatkuvaa kissa ja hiiri -leikkiä. Ratkaisuksi tähän on usein ehdotettu ja sovellettukin erilaisia koneoppimisen menetelmiä, sillä peleistä on helppo...

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Main Author: Kahilainen, Niko
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:fin
Published: 2023
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/92397
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author Kahilainen, Niko
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Kahilainen, Niko Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Kahilainen, Niko Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Kahilainen, Niko
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description FPS-peleissä aimbottien käyttäjillä on epäreilu etu suhteessa rehellisiin pelaajiin, mutta huijausohjelmien käytön havaitseminen perinteisin keinoin on jatkuvaa kissa ja hiiri -leikkiä. Ratkaisuksi tähän on usein ehdotettu ja sovellettukin erilaisia koneoppimisen menetelmiä, sillä peleistä on helppo kerätä monenlaista dataa pelaajien käytöksestä. Tässä tutkielmassa kerätään aineistoa pelisessiosta, ja tutkitaan miten yksinkertainen keinotekoinen neuroverkko onnistuu erottamaan huijausohjelman käyttöä rehellisestä pelaamisesta. Mallin suorituskykyä verrattiin aikaisempaan vastaavaan tutkimukseen aiheesta. Aimbot users in FPS games have an unfair advantage compared to honest players, but cheat detection using traditional methods has spiraled into a neverending game of cat and mouse. One solution to this is the application of machine learning methods, since games make it easy to collect different kinds of data from player behavior. In this thesis, we gather data from live game sessions, and investigate how a simple neural network is able to differentiate cheat usage from honest gameplay. The performance of the model was also compared to previous relevant literature.
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spellingShingle Kahilainen, Niko Aimbottien havaitseminen keinotekoisilla neuroverkoilla Tietotekniikka Mathematical Information Technology 602 neuroverkot koneoppiminen pelit tietokonepelit havaitseminen
title Aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_full Aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_fullStr Aimbottien havaitseminen keinotekoisilla neuroverkoilla Aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_full_unstemmed Aimbottien havaitseminen keinotekoisilla neuroverkoilla Aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_short Aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_sort aimbottien havaitseminen keinotekoisilla neuroverkoilla
title_txtP Aimbottien havaitseminen keinotekoisilla neuroverkoilla
topic Tietotekniikka Mathematical Information Technology 602 neuroverkot koneoppiminen pelit tietokonepelit havaitseminen
topic_facet 602 Mathematical Information Technology Tietotekniikka havaitseminen koneoppiminen neuroverkot pelit tietokonepelit
url https://jyx.jyu.fi/handle/123456789/92397 http://www.urn.fi/URN:NBN:fi:jyu-202312198392
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