Multi-level Anomaly Detection in IoT Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F19%3A10133275" target="_blank" >RIV/63839172:_____/19:10133275 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multi-level Anomaly Detection in IoT Networks

Popis výsledku v původním jazyce

This paper is primarily focused on IoT networks that contain IP devices, such as gateways, sensors,and mobile phones. Our aim is to design scalable monitoring system and its features for IoT gateways, to analyze the behavior of IoT devices, classify them according to trained classes, and to detect anomalies at the network edge. This approach to securing infrastructure brings better visibility and improves threat detection because there is the biggest insight without any obstacles at the network edge. Our goal is to create a system that can notify owners of IoT gateway about suspicious behavior observed even in the encrypted traffic. In our case, anomalous traffic represents some change in the behavior of a device that can be occur after infection by malware or after some configuration changes.

Název v anglickém jazyce

Multi-level Anomaly Detection in IoT Networks

Popis výsledku anglicky

This paper is primarily focused on IoT networks that contain IP devices, such as gateways, sensors,and mobile phones. Our aim is to design scalable monitoring system and its features for IoT gateways, to analyze the behavior of IoT devices, classify them according to trained classes, and to detect anomalies at the network edge. This approach to securing infrastructure brings better visibility and improves threat detection because there is the biggest insight without any obstacles at the network edge. Our goal is to create a system that can notify owners of IoT gateway about suspicious behavior observed even in the encrypted traffic. In our case, anomalous traffic represents some change in the behavior of a device that can be occur after infection by malware or after some configuration changes.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VI20172020079" target="_blank" >VI20172020079: Zabezpečená brána pro internet věcí (SIoT)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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