{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T09:02:59Z","timestamp":1726045379543},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of Russia","award":["FEWM-2020-0037 (TUSUR)"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers\u2019 recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.<\/jats:p>","DOI":"10.3390\/data7020022","type":"journal-article","created":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T04:02:06Z","timestamp":1643515326000},"page":"22","source":"Crossref","is-referenced-by-count":19,"title":["The Comparison of Cybersecurity Datasets"],"prefix":"10.3390","volume":"7","author":[{"given":"Ahmed","family":"Alshaibi","sequence":"first","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia"}]},{"given":"Mustafa","family":"Al-Ani","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia"}]},{"given":"Abeer","family":"Al-Azzawi","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3222-9956","authenticated-orcid":false,"given":"Anton","family":"Konev","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia"}]},{"given":"Alexander","family":"Shelupanov","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rayes, A., and Salam, S. 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