This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

Software-defined networking (SDN) has been recognized for its potential in network programming and centralized control. However, this advancement brings forth critical security vulnerabilities. It is essential to understand that vulnerabilities, by their inherent nature, may lead to potential attacks if not addressed timely and appropriately. In this paper, we introduce a novel multi-modal deep transfer learning (MMDTL) framework tailored for effective attack detection in SDN environments that helps us to investigate a diverse spectrum of attack types. MMDTL framework comprehensively incorporates diverse data modalities — encompassing network traffic patterns, system logs, and user behavior analytic. A pivotal feature of this framework is its transfer learning approach, which enables the assimilation of insights from pre-trained models that subsequently increases the detection performance of attacks. We rigorously analyze the proposed framework with experiments on the intrusion detection evaluation dataset (CIC-IDS2017). Analyses results show the superiority of our framework with a detection accuracy 99.97%.Thus, MMDTL framework has a significant potential to support security in SDNs.