Behavior Anomaly Detection in IoT Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00338644" target="_blank" >RIV/68407700:21240/20:00338644 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-43192-1_53" target="_blank" >https://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>

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
—
Czech description
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Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)
ISBN
978-3-030-43192-1
Number of pages of the result
9
Pages from-to
465-473
Number of pages of the book
1001
Publisher name
Springer International Publishing
Place of publication
Cham
UT code for WoS chapter
—

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