Intrusion Detection and Security Challenges in 6G Networks Using Stochastic Graph Neural Networks

While 5G is famous for its cloudification and micro-services-oriented design, intelligent network orchestration and management are key to the 6G era of networks. Therefore, the 6G paradigm that is being conceived relies heavily 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. 6G communications will allow consumers to interact with online virtual worlds after 2030. For each separate analysis of the Network Intrusion dataset (CIC-IDS-2017), it chooses the optimal subset feature and lowers dimensionality. For the purpose of aggregation, the voting average approach is employed, and two classifiers—LSTM, GNN, RLGNN, and BiGRU—are transformed into DL algorithms. The current classification approach was surpassed by the suggested GNN method. According to the CIC-IDS-2017 Network Intrusion dataset, the accuracy rate was 96.54%.