Unlocking network security and QoS: The fusion of SDN, IoT, and machine learning: A comprehensive analysis

The convergence of Software-Defined Networking (SDN) and the Internet of Things (IoT) has ushered in transformative changes, offering unparalleled levels of network flexibility, programmability, and connectivity. While this integration provides numerous benefits, it also introduces security challenges. Motivated by the imperative to fortify the security posture in this dynamically evolving landscape, this review paper explores the vulnerabilities, threats, and corresponding responses in the security landscape of SDN and IoT. Recognizing the critical need for proactive security measures, the paper underscores the potential of Quality of Service (QoS) empowered by Machine Learning (ML) as a solution. By harnessing ML, QoS emerges as a powerful means to proactively identify and mitigate potential attacks, offering an effective approach to enhance network security. The motivation behind integrating QoS with ML lies in its ability to ensure dependability, availability, and integrity, thereby instilling confidence in the reliability and resilience of the interconnected world.

The paper goes through examination of challenges, delving into the proactive management of QoS within SDN, intricacies of IoT network architectures, and the unique features and limitations of IoT systems. Furthermore, it comprehensively addresses potential countermeasures for various security threats, such as Denial of Service (DOS), Man-in-the-Middle (MITM) attacks, and Ransomware attacks, particularly on devices with limited resources. This abstract provides a concise yet comprehensive overview of the paper's motivations, emphasizing the urgency and significance of the proposed solutions for securing modern network environments.