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Erschienen in: Wireless Personal Communications 3/2023

15.09.2022

IoT Routing Attacks Detection Using Machine Learning Algorithms

verfasst von: Sana Rabhi, Tarek Abbes, Faouzi Zarai

Erschienen in: Wireless Personal Communications | Ausgabe 3/2023

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Abstract

Internet of Things (IoT) is a concept that aims to make the real world more intelligent but susceptible to various attacks. In this paper, we focus on wireless sensor networks (WSNs), as a founding block in the IoT presenting the vulnerability of routing attacks against Routing Protocol for Low power and Lossy Network (RPL). Besides, we discuss some existing research proposals to detect intrusions, and we develop a technique for detecting three types of attacks against RPL. We simulate using Contiki-Cooja four network scenarios one normal and three malicious presenting different attacks, to be able to generate the training and the test sets that are used in the machine learning phase, in which we used WEKA, to decide according to the database whether the behavior is normal or malicious. For this phase, we use different classification algorithms, which enable us to obtain a high precision value that is superior to 96% in all cases.
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Metadaten
Titel
IoT Routing Attacks Detection Using Machine Learning Algorithms
verfasst von
Sana Rabhi
Tarek Abbes
Faouzi Zarai
Publikationsdatum
15.09.2022
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-10022-7

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