Behavior Anomaly Detection in IoT Networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Behavior Anomaly Detection in IoT Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Behavior Anomaly Detection in IoT Networks

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)

ISBN

978-3-030-43192-1

Počet stran výsledku

9

Strana od-do

465-473

Počet stran knihy

1001

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Kód UT WoS kapitoly

—