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.
Highlights
The internet of things (IoT) was introduced to work as a feedback system in terms of adaptability, real-time functionality, intelligence, and predictability
For industrial internet of things (IIoT) applications, it is crucial to keep in mind that this ratio does not hold up, according to the results which reveal that using resampling in combination with different machine learning (ML) classification algorithms improves classification accuracy by more than 10% compared to the state of the art, making the findings more realistic [16]
The majority of attacks on the IoT occur at the data transmission layer, according to the study
Summary
The internet of things (IoT) was introduced to work as a feedback system in terms of adaptability, real-time functionality, intelligence, and predictability. Most importantly, they are practiced in prescriptive matters that include providing decision support and automation [2] In this regard, modern computing technology is used to integrate a large number of cyber and physical components in CPSs. Due to the IoT, a safe and energy-efficient data flow between the physical and digital worlds is maintained [3]. In the fields of computer science and IT, cybersecurity has emerged as a significant research area Even though it initially focused on protecting information systems against attacks from malware, adware, spyware, and ransomware, cybersecurity has since expanded to include intrusion detection systems (IDSs) and firewalls, as well as other technologies. Its aim is to describe in detail the development of the cybersecurity datasets used to train the algorithms that are used for building IDS detection models as well as analyzing and summarizing different and famous IoT attacks Their main security concern is intrusion detection. When it comes to real-world application, these algorithms can fall short
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