Advanced ARP Attack Protection in SDNs Using Deep Learning Approach

Software Defined Networks have developed as a transformative standard in network architecture, providing centralized control, and dynamic supervision of network resources. In recent past several advancements have been made to enhance the capabilities and efficiency of SDN models. A prominent development involves the incorporation of Software-Defined Networking with emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to facilitate smarter and more adaptive network management. Additionally, advancements in SDN security have been a focal point, with the exploration of new techniques to detect and mitigate various types of cyber threats, including those specific to SDN environments However, the openness and flexibility of SDNs also expose them to security vulnerabilities, with Address Resolution Protocol (ARP) attacks being a prominent threat. This research paper investigates the critical challenges posed by most common ARP Protocol attacks such as spoofing attack and Denial-of-Service attacks in contemporary network environments. As these attacks persist as serious threats to network integrity and availability. This research paper explores the use of Deep Learning algorithms for the detection and mitigation of ARP assaults in SDN networks. This research paper presents an in-depth analysis of the challenges posed by ARP attacks in SDNs and propose a novel approach leveraging deep learning techniques for enhanced security. The proposed framework is evaluated through extensive simulations and real-world scenarios, demonstrating its effectiveness in preventing ARP spoofing and DoS attacks.