QEMDD: Quantum Inspired Ensemble Model to Detect and Mitigate DDoS Attacks at Various Layers of SDN Architecture

Software-defined networking (SDN) is considered a next-generation networking model. Several networking components are managed through a centralized controller that enables efficiency and flexibility in configuring network devices, implementing policy decisions, and managing the underlying network in...

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Bibliographic Details
Published in:Wireless personal communications 2022-12, Vol.127 (3), p.2365-2390
Main Authors: Saritha, A., Reddy, B. Ramasubba, Babu, A. Suresh
Format: Article
Language:English
Subjects:
Communications Engineering
Computer architecture
Computer Communication Networks
Cybersecurity
Denial of service attacks
Engineering
Machine learning
Networks
Quantum cryptography
Signal,Image and Speech Processing
Software-defined networking
State-of-the-art reviews
Traffic control
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Summary:Software-defined networking (SDN) is considered a next-generation networking model. Several networking components are managed through a centralized controller that enables efficiency and flexibility in configuring network devices, implementing policy decisions, and managing the underlying network infrastructure through a programmable unit. Despite its default security protocols, SDN is considered to be contradictory towards DDoS attacks. It is observed from state-of-art studies that intrusion in SDN is possible at various layers of its core architecture. Addressing this problem, this article presents a novel ensemble mechanism inspired by quantum cryptography to secure various layers of SDN. This paper presents a two-fold mechanism to secure communications at the SDN architecture's data plane and control plane. It was firstly addressing the secured communication at the data plane, a novel quantum protocol devised. Further, a machine learning-inspired ensemble classifier is devised to detect DDoS attack-prone traffic at the control plane. Simulation studies presented in this article evidenced that the proposed mechanism outperforms the state of art mechanisms in terms of Accuracy and rate of prediction.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-08805-5