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

• Main Authors: Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L.F., Abdulkadir, S.J.
• Format: Article
• Published: MDPI 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122387869&doi=10.3390%2felectronics11020198&partnerID=40&md5=935d3274b03cc9808c51084d26143c60; http://eprints.utp.edu.my/28932/
• Institution: Universiti Teknologi Petronas

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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.