Securing IoT-empowered Fog computing systems: Machine learning perspective
Date
2022
Authors
Ahanger, Tariq Ahamed
Tariq, Usman
Ibrahim, Atef
Ullah, Imdad
Bouteraa, Yassine
Gebali, Fayez
Journal Title
Journal ISSN
Volume Title
Publisher
Mathematics
Abstract
The Internet of Things (IoT) is an interconnected network of computing nodes that can
send and receive data without human participation. Software and communication technology have
advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT
devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a
new era of intelligent devices. However, the rapid expansion has raised security concerns. Another
challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric
strategy results from network congestion, data bottlenecks, and longer response times to security
threats. Fog computing addresses these difficulties by bringing computation to the network edge.
The current research provides a comprehensive review of the IoT evolution, Fog computation,
and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques
for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog
computing security concerns. Additionally, it covers future research objectives in the crucial field of
IoT security.
Description
Keywords
machine learning, security, Fog computing, Internet of Things
Citation
Ahanger, T., Tariq, U., Ibrahim, A., Ullah, I., Bouteraa, Y., & Gebali, F. (2022). “Securing IoT-empowered Fog computing systems: Machine learning perspective.” Mathematics, 10(8), 1298. https://doi.org/10.3390/math10081298