Title: DDoS attack detection and prevention model using pipit flying fox optimisation-based deep neural network

Authors: Anuja Sharma; Parul Saxena

Addresses: Soban Singh Jeena University Almora, SSJU, Almora, Uttarakhand, 263130, India ' Soban Singh Jeena University Almora, SSJU, Almora, Uttarakhand, 263130, India

Abstract: The software-defined network (SDN) remains the futuristic model that helps to satisfy the new application demands of future networks. However, the control panel of SDN is the prime target of destructive attacks, especially distributed denial of service (DDoS). The restrictions in the conventional techniques such as reliability to network topology, low accuracy, and hardware dependencies manifest the need for effective DDoS detection. Hence, the research develops a DDoS attack recognition and prevention model aid with an optimised deep learning network. The significance relies on the pipit flying fox (PPF) optimisation, which selects the optimal hyperparameters, minimises the errors, and accelerates the learning speed. The experimental results are reported as the specificity, sensitivity, and accuracy of 98.5551%, 92.4951%, and 98.4951% respectively for 80% of training. Further, the values are obtained as 98.6397%, 86.0997%, and 98.09972% for specificity, sensitivity, and accuracy respectively at K-fold 10 which exceeds other competent techniques.

Keywords: SDN; software-defined network; DDoS attack; security; attack detection; deep learning; optimisation.

DOI: 10.1504/IJAACS.2025.147720

International Journal of Autonomous and Adaptive Communications Systems, 2025 Vol.18 No.3, pp.220 - 242

Received: 21 Nov 2022
Accepted: 11 Oct 2023
Published online: 28 Jul 2025 *

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