Improved 5G network slicing for enhanced QoS against attack in SDN environment using deep learning

Abstract

Within the evolving landscape of fifth-generation (5G) wireless networks, the introduction of network-slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network-slicing model integrated with an attack detection system within the 5G framework. Leveraging software-defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks-long short-term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real-time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real-world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios.