Detection and mitigation of few control plane attacks in software defined network environments using deep learning algorithm

Summary In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that s the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network contr...

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Bibliographic Details
Published in:Concurrency and computation 2024-11, Vol.36 (26), p.n/a
Main Authors: Kumar, M. Anand, Onyema, Edeh Michael, Sundaravadivazhagan, B., Gupta, Manish, Shankar, Achyut, Gude, Venkataramaiah, Yamsani, Nagendar
Format: Article
Language:English
Subjects:
Algorithms
ARP spoofing
attacks
control channel
controller
CPU
Cybersecurity
Deep learning
flow control
Internet of Things
Machine learning
Network control
Robust control
Software-defined networking
software‐defined‐networks
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Summary:Summary In order to make networks more adaptable and flexible, software‐defined networking (SDN) is an architecture that s the many, easily distinct layers of a network. By enabling businesses and service providers to react swiftly to shifting business requirements, SDN aims to improve network control. SDN has become an important framework for Internet of Things (IoT) and 5G. Despite recent research endeavors focused on pinpointing constraints within SDN design components, various security attacks persist, including man‐in‐the‐middle attacks, host hijacking, ARP poisoning, and saturation attacks. Overcoming these limitations poses a challenge, necessitating robust security techniques to detect and counteract such attacks in SDN environments. This study is dedicated to developing a method for detecting and mitigating control plane attacks within Software Defined Network Environments utilizing Deep Learning Algorithms. The study presents a deep‐learning‐based approach to identifying malicious hosts within SDN networks, thus thwarting unauthorized access to the controller. Experimental results demonstrate the effectiveness of the proposed model in host classification, exhibiting high accuracy and performance compared to alternative approaches.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.8256