Deep Learning For Cyber Security in the Internet of Things (IoT) Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920282" target="_blank" >RIV/00216275:25410/23:39920282 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.35011/IDIMT-2023-391" target="_blank" >http://dx.doi.org/10.35011/IDIMT-2023-391</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.35011/IDIMT-2023-391" target="_blank" >10.35011/IDIMT-2023-391</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning For Cyber Security in the Internet of Things (IoT) Network
Original language description
The Internet of Things (IoT) is a swiftly evolving paradigm having the potential to transform thephysical interaction between individuals and organizations. IoT has applications in multiple fieldssuch as healthcare, education, resource management, and information processing to name a few.Many organizations rely greatly on technology, and most are changing their process into intelligentor smart solutions. Moreover, these networks are wireless, self-configuring, do not need preexisting infrastructure, and have a large unpredictable node movement; security becomes one of themost crucial concerns that need to be addressed. In this paper, we proposed an intrusion preventionmethod that uses a federated deep learning-based framework. A real IoT traffic dataset will be usedto train the state-of-the-art graph neural network algorithm. A comparison will be carried outbased on different experimental results. Finally, this work contributes to the security of IoTnetworks through the implementation of effective tools/techniques for timely IoT attackclassification and mitigation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IDIMT-2023 : New Challenges for ICT and Management : 31st Interdisciplinary Information Management
ISBN
978-3-99151-176-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
391-398
Publisher name
Trauner Verlag
Place of publication
Linz
Event location
Hradec Králové
Event date
Sep 6, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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