Exponential Pine Cone Optimization Enabled Quantum-Inspired Convolutional Neural Networks for Secure Network Slicing in 5G Network
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P123Keywords:
Network slicing, 5G network, Quantum-Inspired Convolutional Neural Networks, Exponential Weight Moving Average, Pine Cone Optimization AlgorithmAbstract
Network Slicing is significant in facilitating a multiple of Fifth-generation (5G) applications, use cases, and services. It provides end-to-end isolation among the slices for customizing each slice in terms of service demands. The increasing demands of 5G networks and advanced features to meet complex business needs have rendered conventional approaches insufficient. Therefore, an efficient approach is required to mitigate the issues in the slicing process. As a result, Exponential Pine Cone Optimization enabled Quantum-Inspired Convolutional Neural Network (EPCO_QuCNet) is introduced for secure network slicing in 5G network. The system model of 5G network is carried out. When network slicing receives requests from a User Equipment (UE), various network parameters, like speed, packet loss rate, jitter, and packet delay budget—are gathered from multiple devices. Hence, a secure slicing of network is classified by Quantum-Inspired Convolutional Neural Networks (QuCNet), which is trained by Exponential Pine Cone Optimization (EPCO) algorithm. Furthermore, the services provided in 5G is accessed by the internet service provider using Virtual Network Function (VNF). EPCO_QuCNet has achieved better outcomes for the evaluation metrics, like acceptance rate, execution time and resource efficiency of 0.927, 0.167 sec and 0.911
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