DDoS Mitigation Using Machine Learning in Software-Defined Networks

Abstract

With the escalating frequency and sophistication of Distributed Denial of Service (DDoS) attacks, the realm of cyber security has witnessed a paradigm shift towards innovative solutions. This review explores the potential of machine learning as a powerful defense mechanism in the field of DDoS mitigation within Software-Defined Networks (SDNs). The study methodically examines a variety of machine learning algorithms used for DDoS mitigation, from cutting-edge deep learning approaches to conventional statistical approaches. A key focus of this review is to provide a comparative analysis of different machine learning approaches, evaluating their efficacy in identifying and mitigating DDoS attacks within SDN environments. The discussion encompasses the strengths and limitations of each algorithm, shedding light on their applicability and performance metrics. By dissecting the nuanced differences between these methodologies, the review aims to guide practitioners and researchers toward informed decisions when implementing DDoS mitigation strategies in SDNs. Furthermore, this study addresses the main challenges faced by machine learning-based DDoS mitigation in SDNs. From issues related to real-time detection and adaptability to dynamic attack patterns to the impact of network scale and diversity, the review systematically outlines these challenges and proposes potential avenues for overcoming them. By understanding these hurdles, stakeholders in the field can proactively develop solutions that enhance the robustness and effectiveness of DDoS mitigation frameworks within SDNs. Conclusively, this review stands as an invaluable resource for cyber security professionals, researchers, and policymakers navigating the intricate terrain of DDoS mitigation in Software-Defined Networks. Through a meticulous exploration of machine learning techniques and a discerning analysis of associated challenges, the paper not only provides comprehensive insights but also lays the groundwork for the development of resilient and adaptive security measures against the ever-evolving landscape of cyber threats. By assimilating the knowledge gleaned from this review, stakeholders are empowered to make informed decisions and contribute to the ongoing refinement of DDoS mitigation strategies, ensuring the continued integrity and security of Software-Defined Networks in the face of emerging threats.