Cyber Attacks in SDN-Based IoT Environment: A Review
Yusra Sh. Ajaj, Bilal R. Al-Kaseem, Yousif Al-Dunainawi · Al-Iraqia Journal of Scientific Engineering Research · 2023
As the Internet of Things (IoT) continues to grow, integrating Software-Defined Networking (SDN) has brought numerous benefits to IoT deployments. However, this convergence also introduces new challenges in terms of cybersecurity. This review paper explores the landscape of cyber-attacks in SDN-based IoT environments, providing an overview of the various attack vectors, vulnerabilities, and potential security risks associated with this emerging paradigm. This paper examines the unique characteristics of SDN-based IoT networks and their implications for cybersecurity. It delves into different types of cyber-attacks that can target SDN-based IoT deployments, including port scanning, Operating System (OS) fingerprinting, fuzzing, Denial-of-Service (DoS), and Distributed Denial-of-Service (DDoS) attacks. In addition, this paper examines the existing research and case studies that leverage Deep Learning (DL) techniques for cyber-attack detection and prevention in SDN-based IoT environments. It highlights the advantages of using DL, including its ability to learn complex patterns and adapt to evolving attack strategies. This paper emphasizes the need for robust datasets and the importance of feature selection and preprocessing techniques to enhance the effectiveness of DL models in the context of SDN-based IoT security. It also discusses the integration of DL with other security measures, such as encryption and access control, to provide a comprehensive defense mechanism. In summary, this review paper contributes to understanding how Artificial Intelligence (AI) can enhance the security of SDN-based IoT environments. It serves as a valuable resource for researchers and practitioners in the field of cyber security, exploring the current research, obstacles, and potential solutions for using DL to identify and mitigate cyber-attacks in SDN-based IoT settings.