Secure Slicing and Allocation of Resources of 5G Networks In Software-Defined Networking / Network Functions Virtualization

Ali Jasim Ramadhan Al-Aameri

doi:10.31436/iiumej.v23i2.1763

Authors

Ali Jasim Ramadhan Al-Aameri University of Alkafeel https://orcid.org/0000-0003-3253-3525

DOI:

https://doi.org/10.31436/iiumej.v23i2.1763

Keywords:

Deep learning, dynamic offloading, network slicing, resource allocation, traffic scheduling

Abstract

In 5G communications, higher data rates and lower latency are needed due to the high traffic rate. Though resource wastage is avoided by secure slicing, sliced networks are exploited by DDoS attackers. Thus, in the present paper, traffic-aware setting up is PRESENTED for resource allocation and secure slicing over the virtualization of 5G networks enabled by software-defined network/network functions. In the proposed method (called T-S³RA), to authenticate user devices, Boolean logic is used with key derivation based on passwords. Moreover, the traffic arrangement is based on the 5G access points. To implement secure resource allocation and network slicing, deep learning models are used. Renyi entropy computation is employed to predict the DDoS attackers. Through the experimental results, the effectiveness of the presented approach is proved.

ABSTRAK: Melalui komunikasi 5G, kadar data yang tinggi dan latensi yang rendah amat diperlukan kerana kadar trafik yang tinggi. Walaupun pembaziran sumber dapat dielakkan melalui pemotongan selamat, rangkaian yang dipotong sering dieksploitasi oleh penyerang DDoS. Oleh itu, kajian ini menyediakan persekitaran sedar-trafik bagi peruntukan sumber dan pemotongan selamat ke atas rangkaian 5G secara maya melalui fungsi rangkaian takrif-perisian. Melaui pendekatan yang dicadangkan (iaitu T-S3RA), peranti pengguna disahkan terlebih dahulu menggunakan logik Boolean dengan perolehan kunci berdasarkan kata laluan. Di samping itu, susunan trafik adalah berdasarkan titik akses 5G. Bagi melaksanakan peruntukan sumber yang selamat dan pemotongan rangkaian, model pembelajaran mendalam telah digunakan. Pengiraan Entropi Renyi dibuat bagi meramal penyerang DDoS. Dapatan eksperimen mengesahkan keberkesanan pendekatan yang dicadangkan.

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