Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Mujaheed Abdullahi; Yahia Baashar; Hitham Alhussian; Ayed Alwadain; Norshakirah Aziz; Luiz Fernando Capretz; Said Jadid Abdulkadir

doi:10.3390/electronics11020198

journal article Open Access Jan 10, 2022

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Mujaheed Abdullahi

Yahia Baashar

Hitham Alhussian Ayed Alwadain Norshakirah Aziz Luiz Fernando Capretz

Said Jadid Abdulkadir

Electronics Vol. 11 No. 2 pp. 198 · MDPI AG

View at Publisher Save 10.3390/electronics11020198

Abstract

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

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Metrics

289

Citations

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References

Details

Published: Jan 10, 2022
Vol/Issue: 11(2)
Pages: 198
License: View

Authors

M

Mujaheed Abdullahi

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

Y

Yahia Baashar

Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia

H

Hitham Alhussian

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

A

Ayed Alwadain

Computer Science Department, Community College, King Saud University, Riyadh 145111, Saudi Arabia

N

Norshakirah Aziz

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

L

Luiz Fernando Capretz

Department of Electrical & Computer Engineering, Western University, London, ON N6A5B9, Canada

S

Said Jadid Abdulkadir

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

Funding

King Saud University Award: RSP-2021/309

Universiti Teknologi Petronas Award: 015LC0-286

Cite This Article

Mujaheed Abdullahi, Yahia Baashar, Hitham Alhussian, et al. (2022). Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review. Electronics, 11(2), 198. https://doi.org/10.3390/electronics11020198

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

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