Hybrid TCP SYN attack detection model in SDN - Muzafar, Saira; Zaman Jhanjhi, Noor (Indonesian Journal of Electrical Engineering and Computer Science, 2025)
Software defined network (SDN) is a developing concept that emerged recently to overcome the constraints of traditional networks. The distinguishing characteristic of SDN is the uncoupling of the control plane from the data plane. This facilitates effective network administration and enables efficient programmability of the network. Nevertheless, the updated architecture is susceptible to cyberattacks including distributed denial of service (DDoS) attacks, that can impair network regular functions and hinder the SDN controller from assisting authorized users. This paper introduces hybrid deep learning model, to detect DDoS assaults triggered by TCP SYN attacks in SDN environments. Our proposed model integrates a temporal convolutional network (TCN) with a stacking classifier that leverages logistic regression, which is an innovative hybrid approach. We assessed the performance of our model by utilizing the benchmark CICDDoS2019 dataset. When compared to other benchmarking techniques, our model significantly improves attack detection. The experimental results indicate that the proposed hybrid model attains 99.9% accuracy for attack detection compared to the available approaches.