The Comparison of Cybersecurity Datasets

Ahmed Alshaibi, Mustafa Al-Ani, Abeer Al-Azzawi, Anton Konev and Alexander Shelupanov
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Ahmed Alshaibi: Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia
Mustafa Al-Ani: Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia
Abeer Al-Azzawi: Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia
Anton Konev: Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia
Alexander Shelupanov: Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems And Radioelectronics, 634000 Tomsk, Russia

Data, 2022, vol. 7, issue 2, 1-18

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’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.

Keywords: cybersecurity; network security; datasets; machine learning; cyberattacks; IoT (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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