Deep Learning Approach to Mitigate DDoS Attacks in SDN

The rise of Distributed Denial of Service (DDoS) attacks remains a significant obstacle to network security and availability. This abstract presents a new hybrid model for DDoS mitigation in Software-Defined Networking (SDN) environments, combining a Semi-supervised Deep Extreme Learning Machine (Semi-Deep ELM) with a hybrid architecture. SDN's centralized control and programmability create an ideal platform for implementing advanced mitigation strategies. The hybrid model proposed integrates the semi-deep ELM approach, utilizing both labeled and unlabeled data to enhance DDoS detection accuracy, along with additional mechanisms for increased resilience and adaptability. By utilizing extreme learning machines and deep learning architectures within a hybrid framework, the model achieves improved robustness and scalability in combating various DDoS attacks as compared to existing models. It also discusses potential challenges and considerations, such as model complexity, resource allocation, and integration with existing network infrastructure. The proposed technique with DP-K-means clustering offers simplicity and efficiency in DDoS attack detection, especially in scenarios with limited labeled data and real-time detection requirements. The adoption of this hybrid model for DDoS mitigation in SDN uses the DP-KMC method for tighter clustering of benign traffic and hence detecting DDoS easily and faster. ERL-AlexNet mitigation provides faster mitigation using n! Wu-Manber algorithm thus presents a promising solution for strengthening network resilience and security, ensuring uninterrupted service delivery, and mitigating potential disruptions in today's dynamic cyber threat landscape. It enables the system to dynamically adapt its mitigation strategies based on evolving attack patterns and network conditions, thereby providing effective protection against a wide array of DDoS threats.