Enhancing Resilience against DDoS Attacks in SDN -based Supply Chain Networks Using Machine Learning
Anass Sebbar, Karim Zkik · 2023
Distributed Denial of Service (DDoS) attacks are becoming increasingly common and sophisticated, and supply chain networks are particularly vulnerable to these types of attacks due to their reliance on interconnected systems. Software-Defined Networking (SDN) has the potential to enhance resilience against DDoS attacks by providing a centralized control mechanism and the ability to reroute traffic dynamically. However, traditional methods for detecting and mitigating DDoS attacks in SDN-based networks may not be sufficient to protect against these threats fully. In this paper, we propose the use of machine learning techniques to detect and mitigate DDoS attacks in SDN-based supply chain networks. We will investigate how the centralized control mechanism of SDN can be leveraged to improve the effectiveness of machine learning-based DDoS attack detection and mitigation. Additionally, we evaluated these techniques' performance and effectiveness and discussed the trade-offs and limitations of using machine learning for DDoS attack detection and mitigation in SDN-based supply chain networks.