A Technical Review of Wireless security for the Internet of things: Software Defined Radio perspective

TL;DR

This paper reviews wireless security vulnerabilities in IoT, focusing on Software Defined Radio (SDR) attacks, highlighting the perception layer's vulnerabilities and proposing future cybersecurity solutions using SDR and AI techniques.

Contribution

It systematically analyzes IoT wireless vulnerabilities and attacks using SDR, and discusses integrating SDR with cognitive and deep learning methods for enhanced security.

Findings

Perception layer is most vulnerable in IoT systems.

SDR offers flexibility for developing advanced security tools.

Combining SDR with AI can improve IoT cybersecurity.

Abstract

The increase of cyberattacks using IoT devices has exposed the vulnerabilities in the infrastructures that make up the IoT and have shown how small devices can affect networks and services functioning. This paper presents a review of the vulnerabilities of the wireless technologies that bear the IoT and assessing the experiences in implementing wireless attacks targeting the Internet of Things using Software-Defined Radio (SDR) technologies. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR radio platforms were compared. On the IoT system model layer, perception layer was identified as the most vulnerable. Most attacks at this level occur due to limitations in hardware, physical exposure of devices, and heterogeneity of technologies. Future cybersecurity systems based on SDR radios have notable advantages due to their flexibility to adapt to new communication technologies and their potential for the development of advanced tools. However, cybersecurity challenges for the Internet of Things are so complex that it is needed to merge SDR hardware with cognitive techniques and intelligent techniques such as deep learning to adapt to rapid technological changes.