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

Charles Oredola, Adnan Ashraf

TL;DR

This work addresses the security of SDN-based IoT networks by performing a Systematic Mapping Study (SMS) over 33 primary studies to characterize security threats, mitigation approaches, controller architectures, datasets, and simulation tools. It reveals a strong emphasis on centralized SDN controllers and machine learning-based defenses, with DDoS, MiTM, and zero-day threats being prominent and a variety of datasets and simulators used to study defenses. While ML approaches are prevalent, the study also highlights gaps in taxonomy, architecture-specific security strategies, and standardized benchmarks. The findings provide a consolidated foundation to guide future research and practice toward more resilient SDN-IoT deployments.

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.

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

TL;DR

This work addresses the security of SDN-based IoT networks by performing a Systematic Mapping Study (SMS) over 33 primary studies to characterize security threats, mitigation approaches, controller architectures, datasets, and simulation tools. It reveals a strong emphasis on centralized SDN controllers and machine learning-based defenses, with DDoS, MiTM, and zero-day threats being prominent and a variety of datasets and simulators used to study defenses. While ML approaches are prevalent, the study also highlights gaps in taxonomy, architecture-specific security strategies, and standardized benchmarks. The findings provide a consolidated foundation to guide future research and practice toward more resilient SDN-IoT deployments.

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.
Paper Structure (43 sections, 4 figures, 8 tables)

This paper contains 43 sections, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. SDN Controller Architecture and IoT
  3. SDN Planes Classification
  4. SDN Controller Configuration
  5. Centralized SDN Controller
  6. Core-Decentralized SDN Controller
  7. Distributed-Decentralized SDN Controller
  8. IoT Architecture Overview
  9. Perception Layer
  10. Network Layer
  11. Middleware Layer
  12. Application Layer
  13. Security Layer
  14. Management and Orchestration Layer
  15. Study Design
  16. ...and 28 more sections

Figures (4)

  • Figure 1: The SMS Process Flow.
  • Figure 2: The distribution of the publication types per number of publications considered in the selected primary studies.
  • Figure 3: The distribution of the attack types to the mitigation approach
  • Figure 4: The distribution of the data analysis techniques used to the dataset types considered in the selected primary studies.