Intrusion detection stops leaning on correlation alone: causal graph plus entropy compression cuts 78 flow features to 18 and reaches 97.4% accuracy
A hybrid intrusion-detection framework that chains causal graph learning, entropy-guided compression, attention fusion, adversarial calibration and trust-weighted federated aggregation into one pipeline reaches 97.4% detection accuracy on CICIDS2017 while cutting the input from 78 features to 18.