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CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experie...

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Autores principales: Neto, Euclides Carlos Pinto, Dadkhah, Sajjad, Ferreira, Raphael, Zohourian, Alireza, Lu, Rongxing, Ghorbani, Ali A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346235/
https://www.ncbi.nlm.nih.gov/pubmed/37447792
http://dx.doi.org/10.3390/s23135941
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author Neto, Euclides Carlos Pinto
Dadkhah, Sajjad
Ferreira, Raphael
Zohourian, Alireza
Lu, Rongxing
Ghorbani, Ali A.
author_facet Neto, Euclides Carlos Pinto
Dadkhah, Sajjad
Ferreira, Raphael
Zohourian, Alireza
Lu, Rongxing
Ghorbani, Ali A.
author_sort Neto, Euclides Carlos Pinto
collection PubMed
description Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.
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spelling pubmed-103462352023-07-15 CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment Neto, Euclides Carlos Pinto Dadkhah, Sajjad Ferreira, Raphael Zohourian, Alireza Lu, Rongxing Ghorbani, Ali A. Sensors (Basel) Article Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website. MDPI 2023-06-26 /pmc/articles/PMC10346235/ /pubmed/37447792 http://dx.doi.org/10.3390/s23135941 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Neto, Euclides Carlos Pinto
Dadkhah, Sajjad
Ferreira, Raphael
Zohourian, Alireza
Lu, Rongxing
Ghorbani, Ali A.
CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title_full CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title_fullStr CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title_full_unstemmed CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title_short CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment
title_sort ciciot2023: a real-time dataset and benchmark for large-scale attacks in iot environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346235/
https://www.ncbi.nlm.nih.gov/pubmed/37447792
http://dx.doi.org/10.3390/s23135941
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