Secure SDN-based IoT Networks for Drought Management: Mitigating DDoS Attacks with Machine Learning

Traditional networks are often rigid and expensive to upgrade, posing challenges for innovation and adaptation to evolving requirements. Software-defined networking (SDN) emerges as a solution to this dilemma, offering enhanced flexibility and scalability. However, the security vulnerabilities inherent in SDN-based Internet of Things (IoT) networks, particularly in critical sectors like agriculture, present significant concerns. During a drought, critical infrastructures, such as water distribution systems in agricultural environments, could be targeted by DDoS attacks, further exacerbating the crisis. Additionally, these attacks could serve as distractions, diverting the focus of authorities and farmers away from necessary measures to manage the drought. Moreover, IoT devices used for monitoring meteorological conditions during the drought could also be compromised and exploited in DDoS attacks. To address this vulnerability, a proposed mechanism combines machine learning (ML) with SDN to detect and mitigate distributed denial of service (DDoS) attacks in agricultural environments. This approach operates in three phases: building a robust detection model using ML techniques, integrating this model into the SDN controller for real-time anomaly detection, and implementing mitigation strategies to prevent further damage in case of an attack. Performance evaluation using accuracy performance metrics has demonstrated remarkable results, achieving 99.97% accuracy in detecting DDoS attacks with real-world datasets. This innovative approach not only enhances the security of agricultural networks but also ensures the uninterrupted operation of critical surveillance systems, thereby safeguarding food production and supply chains.