A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks

TL;DR

This paper systematically reviews how SDN controllers are used to improve IoT network security, highlighting current trends, common architectures, and the use of machine learning for risk mitigation.

Contribution

It provides a comprehensive overview of the state-of-the-art in SDN-based IoT security, identifying research gaps and analyzing 33 primary studies.

Findings

Centralized SDN controller architecture is most common for IoT security.

Machine learning is the predominant technique for risk mitigation.

Research trends focus on security enhancement and threat detection.

Abstract

Context: The increase in Internet of Things (IoT) devices gives rise to an increase in deceptive manipulations by malicious actors. These actors should be prevented from targeting the IoT networks. Cybersecurity threats have evolved and become dynamically sophisticated, such that they could exploit any vulnerability found in IoT networks. However, with the introduction of the Software Defined Network (SDN) in the IoT networks as the central monitoring unit, IoT networks are less vulnerable and less prone to threats. %Although, the SDN itself is vulnerable to several threats. Objective: To present a comprehensive and unbiased overview of the state-of-the-art on IoT networks security enhancement using SDN controllers. Method: We review the current body of knowledge on enhancing the security of IoT networks using SDN with a Systematic Mapping Study (SMS) following the established guidelines. Results: The SMS result comprises 33 primary studies analyzed against four major research questions. The SMS highlights current research trends and identifies gaps in the SDN-IoT network security. Conclusion: We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture. However, this architecture is not without its limitations. Additionally, the predominant technique utilized for risk mitigation is machine learning.