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Embedding Security Awareness for Virtual Resource Allocation in 5G Hetnets Using Reinforcement Learning

Cao, Haotong; Aujla, Gagangeet Singh; Garg, Sahil; Kaddoum, Georges; Yang, Longxiang

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Haotong Cao

Sahil Garg

Georges Kaddoum

Longxiang Yang


n the 5G era, heterogeneous networks (Het-Nets) are designed for achieving data rates and customized service demands. To realize this, virtualization technologies are widely accepted as enablers for implementing 5G HetNets, aimed at managing and scheduling virtualized physical resources in a flexible manner. However, the major focus of the existing research lies in the effective allocation of virtual resources and maximizing the number of implemented network services, ignoring the virtual resource security issues. However, the security threats and vulnerabilities due to the complexity of virtualization can lead to major performance outbreaks and information leakage. Therefore, this article attempts to tackle the security issues in 5G HetNets virtual resource allocation. The article starts from modeling the major security attacks for virtual resource allocation through comprehensive discussion on the typical types of security attacks. Following the attack model, a novel secure framework (VRA-RL-SecAwa) based on the emerging reinforcement learning approach, is presented. The proposed VRA-RL-SecAwa framework works in different phases, 1. Reinforcement-learning-based preliminary security preparation 2. Greedy-approach-based secure virtual node resource allocation embedding 3. Secure and shortest path virtual link resource allocation scheme 4. Network reconfiguration and update The proposed VRA-RL-SecAwa framework is evaluated through extensive simulations in order to demonstrate its efficiency and effectiveness. The results obtained validate the superiority of the proposed framework in contrast to existing variants of its category.


Cao, H., Aujla, G. S., Garg, S., Kaddoum, G., & Yang, L. (2021). Embedding Security Awareness for Virtual Resource Allocation in 5G Hetnets Using Reinforcement Learning. IEEE Communications Standards Magazine, 5(2), 20-27.

Journal Article Type Article
Online Publication Date Jun 25, 2021
Publication Date 2021-06
Deposit Date Sep 10, 2021
Publicly Available Date Sep 20, 2021
Journal IEEE Communications Standards Magazine
Print ISSN 2471-2825
Electronic ISSN 2471-2833
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 5
Issue 2
Pages 20-27


Accepted Journal Article (640 Kb)

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