Internet of Things botnets: A survey on Artificial Intelligence based detection techniques
Lefoane, Moemedi ; Ghafir, Ibrahim ; ;
Lefoane, Moemedi
Ghafir, Ibrahim
Publication Date
2025-04
End of Embargo
Supervisor
Rights
© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-01-13
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
The Internet of Things (IoT) is a game changer when it comes to digitisation across industries. The Fourth Industrial Revolution (4IR), brought about a paradigm shift indeed, unlocking possibilities and taking industries to greater heights never reached before in terms of cost saving and improved performance leading to increased productivity and profits, just to mention a few. While there are more benefits provided by IoT, there are challenges arising from the complexities, limitations and requirements of IoT and key enabling technologies. Distributed Denial of Service (DDoS) attacks are among the most prevalent and dominant cyber-attacks that have been making headlines repeatedly in recent years. IoT technology has increasingly become the preferred technology for delivering these cyber-attacks. It does not come as a surprise that IoT devices are an attractive target for adversaries, as they are easy to compromise due to inherent limitations and given that they are deployed in large numbers. This paper reviews IoT botnet detection approaches proposed in recent years. Furthermore, IoT ecosystem components are outlined, revealing their challenges, limitations and key requirements that are vital to securing the whole ecosystem. These include cloud computing, Machine Learning (ML) and emerging wireless technologies: 5G and 6G.
Version
Published version
Citation
Lefoane M, Ghafir I, Kabir S et al (2025) Internet of Things botnets: A survey on Artificial Intelligence based detection techniques. Journal of Network and Computer Applications. 236: 104110.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Article