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• AEGIS achieves an F1-score of 0.9952 and 99.50% True Positive Rate for detecting adversarial network traffic.result 95%
• AEGIS processes 64,000-packet swarms at line-rate with 262 us inference latency on an RTX 4090.technical 90%
• Traditional Euclidean Transformer-based classifiers (e.g., ET-BERT) are vulnerable to byte-level adversarial morphing, with pre-padding attacks reducing ET-BERT accuracy to 25.68%.limitation 85%
• AEGIS discards payload bytes in favor of 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold for detection.method 90%
• AEGIS uses a Thermodynamic Variance Detector to compute sequence-wide Shannon Entropy to expose automated C2 tunnel anomalies.technical 85%
• The system uses a pure C++ eBPF Harvester with zero-copy IPC to bypass the Python GIL limitation.technical 80%
• AEGIS was evaluated on a 400GB, 4-tier adversarial corpus spanning backbone traffic, IoT botnets, zero-days, and proprietary VLESS Reality tunnels.method 90%
• VLESS Reality bypasses certificate-based detection entirely, creating a need for new detection approaches.limitation 80%

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