Advancing AI-Based Security Mechanisms for 5G Networks: Overcoming Privacy Concerns

Artificial Intelligence and machine learning are critical components in ramping up security for 5G networks - and later 6 G ones. With the introduction of 5 G, revolutionizing various sectors, the paper emphasizes the paramount importance of innovative security mechanisms to safeguard these critical infrastructures against sophisticated cyber threats. Naive Bayes, Random Forest, K-Nearest Neighbor algorithm, Decision Trees Multi-Layer Perceptrons are all considered for inclusion and application of various machine learning techniques. It explains in detail how each of these have been used so far in detecting network intrusion. While acknowledging limitations such as the Naive Bayes model’s performance and misclassification issues, the study reviews the results and assesses the benefits and drawbacks of different machine learning algorithms, including Non-Linear SVM, Sequential Neural Networks, Recurrent Neural Networks, Artificial Neural Networks, Convolutional Neural Networks. The findings underscore the significance of traditional methods like SVMs and the need for optimization in the case of RNNs. Ultimately, this study contributes to enhancing the security and resilience of advanced wireless networks in a dynamic threat landscape.