Secure flow authentication and CNN-based detection for DDoS mitigation in SDN

Dandotiya, Monika; Khunteta, Ajay; Sankhla, Kriti; Sharma, Manoj Kumar; Hati, Aditya Narayan

doi:10.47974/JDMSC-2628

Open Access Research Article

Secure flow authentication and CNN-based detection for DDoS mitigation in SDN

Monika Dandotiyadandotiyamonika@gmail.comDepartment of Computer Science & EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Ajay KhuntetaAjay.khunteta@poornima.edu.inDepartment of Computer Science & EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Kriti Sankhlakriti.sankhla@gmail.comDepartment of Computer Science & EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , ^*Manoj Kumar SharmaCorresponding authormanojkumar.sharma@jaipur.manipal.eduDepartment of Computer and Communication EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Aditya Narayan Hatiaditya.narayan@jaipur.manipal.eduDepartment of Computer and Communication EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1503–1519Vol. 29Issue 3March 2026DOI: 10.47974/JDMSC-2628 Crossmark XML

Received:: 01 Sep 2025
Published Online:: 13 Mar 2026
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2628
Pages:: 1503–1519

Abstract

By separating control plane from data plane, Software-Defined Networking has revolutionized network management by facilitating centralized control and increased network adaptability. DDoS in particular, are security flaws introduced by SDN that have the potential to seriously impair network services. This study presents a novel two-phase method for using CNN to increase the security of SDN settings. Establishing a secure authentication method that stops unauthorized nodes from accessing network services is the first step in reducing the risk of unauthorized access. The identification and mitigation of DDoS attacks launched by verified nodes is the focus of the second phase. For real-time traffic analysis, a CNN-based detection system is developed, trained on multiple datasets, including CICDDoS2019, and integrated into the SDN controller. Comprehensive tests utilizing simulation tools show how effective the suggested methodology is at improving detection accuracy, lowering false positive rates, and preserving network performance. This study provides a flexible, effective, and reliable approach to enhance DDoS detection and mitigation in SDN systems.

Keywords

SDN DDoS Attacks detection Cyber security Classification Deep learning Convolutional neural network (CNN)

Subject Classifications

68M2568P25

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