FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä

Vaikka esineiden internet onkin tapana hyödyntää internetiä vielä suhteellisen uusi, kasvaa käytössä olevien IoT-laitteiden määrä jatkuvasti. Samalla kun nämä laitteet tulevat yhä enemmän osaksi jokapäiväistä elämäämme, korostuu niiden tietoturvan merkitys. Poikkeamia verkkoliikenteestä tunnistava t...

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Main Author: Reinikainen, Jani
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:fin
Published: 2022
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/80549
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author Reinikainen, Jani
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Reinikainen, Jani Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Reinikainen, Jani Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Reinikainen, Jani
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description Vaikka esineiden internet onkin tapana hyödyntää internetiä vielä suhteellisen uusi, kasvaa käytössä olevien IoT-laitteiden määrä jatkuvasti. Samalla kun nämä laitteet tulevat yhä enemmän osaksi jokapäiväistä elämäämme, korostuu niiden tietoturvan merkitys. Poikkeamia verkkoliikenteestä tunnistava tunkeutumisen havaitsemisjärjestelmä voi osaltaan parantaa tietoturvaa hälyttämällä poikkeavasta verkkoliikenteestä. Tässä työssä selvitettiin, miten hyvin FAMDAD-menetelmä soveltuu puoliohjattuun ja ohjaamattomaan poikkeaman tunnistukseen ensisijaisesti IoT-verkoista kerätyistä liikennevirtatietueisiin pohjautuvista aineistoista. Työn empiirisen osuuden tulosten perusteella FAMDAD-menetelmällä saatujen tulosten ei voitu osoittaa poikkeavan tilastollisesti merkitsevästi Mahalanobiksen etäisyydellä ja autoenkoodereihin perustuneella menetelmällä saaduista tuloksista. Although the Internet of Things is still relatively new, the number of IoT devices in use is constantly increasing. As these devices become more and more ubiquitous, the importance of their security is being emphasized. An intrusion detection system that detects anomalies from network traffic can improve security by alerting about anomalous network traffic. The suitability of FAMDAD for semisupervised and unsupervised anomaly detection from network traffic flow record based data collected primarily from IoT networks was investigated in this work. Based on the results of the empirical comparison, it could not be shown that the results obtained using FAMDAD differ statistically significantly from the results obtained by Mahalanobis distance and simple autoencoders.
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Ty\u00f6n empiirisen\nosuuden tulosten perusteella FAMDAD-menetelm\u00e4ll\u00e4 saatujen tulosten ei\nvoitu osoittaa poikkeavan tilastollisesti merkitsev\u00e4sti Mahalanobiksen et\u00e4isyydell\u00e4\nja autoenkoodereihin perustuneella menetelm\u00e4ll\u00e4 saaduista tuloksista.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Although the Internet of Things is still relatively new, the number of IoT\ndevices in use is constantly increasing. As these devices become more and more\nubiquitous, the importance of their security is being emphasized. An intrusion\ndetection system that detects anomalies from network traffic can improve security\nby alerting about anomalous network traffic. The suitability of FAMDAD for semisupervised\nand unsupervised anomaly detection from network traffic flow record\nbased data collected primarily from IoT networks was investigated in this work. 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spellingShingle Reinikainen, Jani FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä autoenkooderi FAMD FAMDAD IoT Mahalanobiksen etäisyys PCA poikkeaman tunnistus tunkeutumisen havaitsemisjärjestelmä Tietotekniikka Mathematical Information Technology 602 Internet tietoturva esineiden internet protokollat tietoverkot TCP/IP ARP tietoliikenne menetelmät neuroverkot
title FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
title_full FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
title_fullStr FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
title_full_unstemmed FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
title_short FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
title_sort famdad ja poikkeamien tunnistaminen iot verkkoliikenteestä
title_txtP FAMDAD ja poikkeamien tunnistaminen IoT-verkkoliikenteestä
topic autoenkooderi FAMD FAMDAD IoT Mahalanobiksen etäisyys PCA poikkeaman tunnistus tunkeutumisen havaitsemisjärjestelmä Tietotekniikka Mathematical Information Technology 602 Internet tietoturva esineiden internet protokollat tietoverkot TCP/IP ARP tietoliikenne menetelmät neuroverkot
topic_facet 602 ARP FAMD FAMDAD Internet IoT Mahalanobiksen etäisyys Mathematical Information Technology PCA TCP/IP Tietotekniikka autoenkooderi esineiden internet menetelmät neuroverkot poikkeaman tunnistus protokollat tietoliikenne tietoturva tietoverkot tunkeutumisen havaitsemisjärjestelmä
url https://jyx.jyu.fi/handle/123456789/80549 http://www.urn.fi/URN:NBN:fi:jyu-202204112227
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