Machine Learning-based Secure 5G Network Slicing: A Systematic Literature Review

• DOI: 10.14569/ijacsa.2023.0141239
• Publication Date: 2023-12-29T14:08:21Z
• Authors: Meshari Huwaytim ...
• Keywords: Provisioning

As the fifth-generation (5G) wireless networks continue to advance, concept of network slicing has gained significant attention for enabling provisioning diverse services tailored specific application requirements. However, security concerns associated with pose challenges that demand comprehensive exploration and analysis. In this paper, we present a systematic literature review critically examines existing body research on machine learning techniques securing 5G slicing. Through an extensive analysis wide range scholarly articles selected from search databases, identify classify key approaches proposed enhancing in environment. We investigate these based their effectiveness addressing various threats vulnerabilities while considering factors such as accuracy, scalability, efficiency. Our reveals techniques, including deep algorithms, have been anomaly detection, intrusion authentication observe face related accuracy under dynamic heterogeneous conditions, scalability when dealing large number slices, efficiency terms computational complexity resource utilization. To overcome challenges, our experimentation shows integration reinforcement CNNs, multi-agent learning, distributed SVM frameworks emerged potential solutions improved Furthermore, promising directions, hybrid models, adoption explainable AI investigation privacy-preserving mechanisms.