Sahil Garg
Security in IoT-Driven Mobile Edge Computing: New Paradigms, Challenges, and Opportunities
Garg, Sahil; Kaur, Kuljeet; Kaddoum, Georges; Garigipati, Prasad; Aujla, Gagangeet Singh
Authors
Kuljeet Kaur
Georges Kaddoum
Prasad Garigipati
Dr Gagangeet Aujla gagangeet.s.aujla@durham.ac.uk
Associate Professor in Computer Science
Abstract
With the exponential growth in the number of connected devices, in recent years there has been a paradigm shift toward mobile edge computing. As a promising edge technology, it pushes mobile computing, network control, and storage to the network edges so as to provide better support to computation-intensive Internet of Things (IoT) applications. Although it enables offloading latency-sensitive applications at the resource-limited mobile devices, decentralized architectures and diversified deployment environments bring new security and privacy challenges. This is due to the fact that, with wireless communications, the medium can be accessed by both legitimate users and adversaries. Though cloud computing has helped in substantial transformation of global business, it falls short in provisioning distributed services, namely, security of IoT systems. Thus, the ever-evolving IoT applications require robust cyber-security measures particularly at the network's edge, for widespread adoption of IoT applications. In this vein, the classic machine learning models devised during the last decade, fall short in terms of low accuracy and reduced scalability for real-time attack detection across widely dispersed edge nodes. Thus, the advances in areas of deep learning, federated learning, and transfer learning could mark the evolution of more sophisticated models that can detect cyberattacks in heterogeneous IoT-driven edge networks without human intervention. We provide a SecEdge-Learn Architecture that uses deep learning and transfer learning approaches to provided a secure MEC environment. Moreover, we utilized blockchain to store the knowledge gained from the MEC clusters and thereby realizing the transfer learning approach to utilize the knowledge for handling different attack scenarios. Finally, we discuss the industry relevance of the MEC environment.
Citation
Garg, S., Kaur, K., Kaddoum, G., Garigipati, P., & Aujla, G. S. (2021). Security in IoT-Driven Mobile Edge Computing: New Paradigms, Challenges, and Opportunities. IEEE Network, 35(5), 298-305. https://doi.org/10.1109/mnet.211.2000526
Journal Article Type | Article |
---|---|
Online Publication Date | Sep 15, 2021 |
Publication Date | 2021-09 |
Deposit Date | Nov 7, 2021 |
Publicly Available Date | Nov 8, 2021 |
Journal | IEEE Network |
Print ISSN | 0890-8044 |
Electronic ISSN | 1558-156X |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 35 |
Issue | 5 |
Pages | 298-305 |
DOI | https://doi.org/10.1109/mnet.211.2000526 |
Public URL | https://durham-repository.worktribe.com/output/1224684 |
Files
Accepted Journal Article
(803 Kb)
PDF
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Software Defined Internet of Everything
(2022)
Book
Edge Computing Based Resource Supplementation for Software Defined Vehicular Networks
(2022)
Presentation / Conference Contribution
A Federated Leaning Perspective for Intelligent Data Communication Framework in IoT Ecosystem
(2022)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search