Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä

DDoS-hyökkäykset estävät käyttäjien pääsyn jaettuihin palveluihin, ja ne toteutetaan useasti hajautetun IoT-laiteverkon avulla. IoT-laitteiden tietoturva on puutteellinen, joten hyökkääjät hyödyntävät niitä DDoS-hyökkäysten toteuttamiseen. Tutkielmassa tarkastellaan kone- ja syväoppimisen käyttöä D...

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Main Author: Toppi, Samuli
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Bachelor's thesis
Language:fin
Published: 2024
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/94878
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author Toppi, Samuli
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Toppi, Samuli Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Toppi, Samuli Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Toppi, Samuli
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description DDoS-hyökkäykset estävät käyttäjien pääsyn jaettuihin palveluihin, ja ne toteutetaan useasti hajautetun IoT-laiteverkon avulla. IoT-laitteiden tietoturva on puutteellinen, joten hyökkääjät hyödyntävät niitä DDoS-hyökkäysten toteuttamiseen. Tutkielmassa tarkastellaan kone- ja syväoppimisen käyttöä DDoS-hyökkäysten havaitsemisessa ja torjunnassa IoT-ympäristössä. Kone- ja syväoppimismenetelmiä käytetään tunkeutumisen havaitsemisjärjestelmien rakentamisessa. DDoS attacks deny users access to shared services and are often carried out using a distributed network of IoT devices. The security of IoT devices is inadequate, so attackers exploit them to carry out DDoS attacks. The thesis examines the use of machine- and deep learning in detecting and mitigating DDoS attacks in an IoT environment. Machine- and deep learning methods are used to build intrusion detection systems.
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spellingShingle Toppi, Samuli Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä palvelunestohyökkäykset DDoS bottiverkko IDS Tietotekniikka Mathematical Information Technology 602 esineiden internet tietotekniikka tietoverkot tieto- ja viestintätekniikka tietoturva koneoppiminen syväoppiminen
title Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
title_full Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
title_fullStr Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
title_full_unstemmed Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
title_short Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
title_sort kone ja syväoppiminen iot laitteiden ddos hyökkäyksissä
title_txtP Kone- ja syväoppiminen IoT-laitteiden DDoS-hyökkäyksissä
topic palvelunestohyökkäykset DDoS bottiverkko IDS Tietotekniikka Mathematical Information Technology 602 esineiden internet tietotekniikka tietoverkot tieto- ja viestintätekniikka tietoturva koneoppiminen syväoppiminen
topic_facet 602 DDoS IDS Mathematical Information Technology Tietotekniikka bottiverkko esineiden internet koneoppiminen palvelunestohyökkäykset syväoppiminen tieto- ja viestintätekniikka tietotekniikka tietoturva tietoverkot
url https://jyx.jyu.fi/handle/123456789/94878 http://www.urn.fi/URN:NBN:fi:jyu-202405163647
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