Behavior Anomaly Detection in IoT Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F19%3A10133251" target="_blank" >RIV/63839172:_____/19:10133251 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-43192-1_53" target="_blank" >http://dx.doi.org/10.1007/978-3-030-43192-1_53</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-43192-1_53" target="_blank" >10.1007/978-3-030-43192-1_53</a>
Alternative languages
Result language
angličtina
Original language name
Behavior Anomaly Detection in IoT Networks
Original language description
Data encryption makes deep packet inspection less suitable nowadays, and the need of analyzing encrypted traffic is growing. Machine learning brings new options to recognize a type of communication despite the heterogeneity of encrypted IoT traffic right at the network edge. We propose the design of scalable architecture and the method for behavior anomaly detection in IoT networks. Combination of two existing semi-supervised techniques that we used ensures higher reliability of anomaly detection and improves results achieved by a single method. We describe conducted classification and anomaly detection experiments allowed thanks to existing and our training datasets. Presented satisfying results provide a subject for further work and allow us to elaborate on this idea.
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
20206 - Computer hardware and architecture
Result continuities
Project
<a href="/en/project/VI20172020079" target="_blank" >VI20172020079: Secure Gateway for the Internet of Things (SIoT)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)
ISBN
978-3-030-43192-1
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
465-473
Publisher name
Springer
Place of publication
Neuveden
Event location
Madurai, India
Event date
Dec 19, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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