DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm

Name: DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm
Author: Tarlan, Ozan, Şafak, I., Çakmakçı, Kübra Kalkan

İsim	DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm
Yazar	Tarlan, Ozan, Şafak, I., Çakmakçı, Kübra Kalkan
Basım Tarihi:	2022
Basım Yeri	- IEEE Computer Society
Konu	Distributed ledger technology, Internet of things, Machine learning, Network security
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166541332-9
Kayıt Numarası	a22111da-f6a0-489c-81df-c653531c1e80
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise the above requirements. This paper presents a new reliable blacklisting protocol for ensuring the Internet of Things (IoT) network security and mitigating the effects of interference caused by multipath Rayleigh fading using a distributed approach. The proposed blacklisting protocol is simulated over a distributed IoT network setup where flat Rayleigh fading disrupts Message Queuing Telemetry Transport (MQTT) communications. Distributed servers jointly decide in real-Time whether to blacklist a device after individually performing anomaly detection and submitting their results to the hashgraph network. The IoT devices are classified by a device fingerprinting method using various machine learning (ML) algorithms that are trained with real-Time packet capture data. The proposed blacklisting protocol is shown to increase the accuracy of blacklisting malignant devices from 42% to 82% as the number of servers increases from one to five for mixed attacks. It also achieves higher accuracies ranging between 47.2%-97.6% versus 47.4%-90.7% compared to the related work for Denial of Service (DoS) attacks. The proposed protocol is particularly suitable for the Industrial IoT (IIoT) in mitigating the effects of harsh communication environments in manufacturing facilities.
DOI	10.1109/ICOIN53446.2022.9687198
Cilt	2022

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DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm

Yazar Tarlan, Ozan, Şafak, I., Çakmakçı, Kübra Kalkan

Basım Tarihi 2022

Basım Yeri - IEEE Computer Society

Konu Distributed ledger technology, Internet of things, Machine learning, Network security

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166541332-9

Kayıt Numarası a22111da-f6a0-489c-81df-c653531c1e80

Lokasyon Computer Science

Tarih 2022

Örnek Metin Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise the above requirements. This paper presents a new reliable blacklisting protocol for ensuring the Internet of Things (IoT) network security and mitigating the effects of interference caused by multipath Rayleigh fading using a distributed approach. The proposed blacklisting protocol is simulated over a distributed IoT network setup where flat Rayleigh fading disrupts Message Queuing Telemetry Transport (MQTT) communications. Distributed servers jointly decide in real-Time whether to blacklist a device after individually performing anomaly detection and submitting their results to the hashgraph network. The IoT devices are classified by a device fingerprinting method using various machine learning (ML) algorithms that are trained with real-Time packet capture data. The proposed blacklisting protocol is shown to increase the accuracy of blacklisting malignant devices from 42% to 82% as the number of servers increases from one to five for mixed attacks. It also achieves higher accuracies ranging between 47.2%-97.6% versus 47.4%-90.7% compared to the related work for Denial of Service (DoS) attacks. The proposed protocol is particularly suitable for the Industrial IoT (IIoT) in mitigating the effects of harsh communication environments in manufacturing facilities.

DOI 10.1109/ICOIN53446.2022.9687198

Cilt 2022