Title: A top-down survey on securing IoT with machine learning: goals, recent advances and challenges

Authors: Sania Iqbal; Shaima Qureshi

Addresses: Department of Computer Science and Engineering, National Institute of Technology (Srinagar), Srinagar, Jammu & Kashmir, India ' Department of Computer Science and Engineering, National Institute of Technology (Srinagar), Srinagar, Jammu & Kashmir, India

Abstract: The Internet of Things (IoT) has seen it all from being just another innovation to a leading technology; it is now a binding force that interconnects various aspects of our lives. The IoT's tremendous growth is driven by emerging applications and evolving business models, reinforced by falling cost resources and computing. However, it's heterogeneous nature and restricted existence raise various security concerns for the network operators, IoT service providers, and consumers alike. Machine learning has established itself as an inherent part of several IoT applications and is now taking over to address IoT's critical security challenges. The use of machine learning for securing IoT provides the IoT infrastructure with autonomously updated real-time security features. This paper performs a top-down survey on various active attacks faced by IoT networks and aims to showcase how machine learning has become an integral part of the IoT security model.

Keywords: IoT; internet of things; IoT security; IoT security goals; machine learning; machine learning algorithms; deep learning; active attacks; security challenges.

DOI: 10.1504/IJWMC.2022.122484

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.1, pp.38 - 55

Received: 18 Jun 2021
Accepted: 07 Feb 2022
Published online: 27 Apr 2022 *

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