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Traditional machine learning models outperform large language models in 5G intrusion detection, achieving near-perfect accuracy with far less computational cost.
Anomalies in network traffic reveal critical dependency violations that can enhance detection and understanding of attacks, outperforming traditional methods with up to 7x faster inference.
