Improving 6G Network Safety, Privacy, and Resource Efficiency via the Application of Machine Learning and Network Data Analytics

The 6G era of networks is tightly tied to intelligent network orchestration while administration, but the cloudification of networks using a micro-services-oriented architecture is a well-known element of 5G. It follows that the proposed 6G paradigm heavily relies on AI, ML, and DL. Proactive threat identification, smart mitigation tactics, and assurance that 6G networks would be self-sustaining are essential for future end-to-end network automation. In order to solidify and enhance AI's position in 6G network security, this article delves at potential AI applications in 6G security. To get there, our research reworked an existing 6G network anomaly detection system to make it operate with ensemble learning (EL) to communicate networks. In order to protect 6G user data from unauthorized access, resale, intoxication, and free-riding attacks, we offer a thorough framework for detecting data attacks that is optimized for secure aggregation using the Deep Reinforcement learning - PPO algorithm. To guard against data poisoning, unauthorized data sources, and mistakes in data aggregation, we use a set of encrypted data auditing tools. We have also performed analysis, evaluated many prospective security strategies, and suggested the best security measures for certain 6G situations. the Krull Herd-Advanced Encryption Standard (KH-AES) algorithm for secure data transmissions. To use IDS, this study makes use of the NSL-KDD dataset. Probing, normal, U2R, DoS, R2L, and unidentified were the six assaults used to categorize the data. Classification results are heavily affected by the weights in HCNN layers. When compared to well-known methods, the suggested system outperforms them when it comes of FS, classifications, and data sharing security.