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

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

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