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AEGIS, a novel network intrusion detection system, addresses the vulnerability of Euclidean Transformer-based classifiers to adversarial morphing attacks by modeling network traffic as a thermodynamic system. It uses a Thermodynamic Variance-Guided Hyperbolic Liquid State Space Model (TVD-HL-SSM) operating on 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold, measuring microsecond Inter-Arrival Time (IAT) decay and sequence-wide Shannon Entropy. AEGIS achieves a 0.9952 F1-score and 99.50% True Positive Rate at 262 us inference latency on an RTX 4090, demonstrating state-of-the-art performance against a diverse adversarial corpus.
Forget byte-level inspection – AEGIS models network traffic as a thermodynamic system to achieve near-perfect detection of zero-day exploits and adversarial attacks, even against sophisticated VLESS Reality tunnels.
As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers (e.g., ET-BERT) for encrypted traffic analysis. However, these models remain vulnerable to byte-level adversarial morphing -- recent pre-padding attacks reduced ET-BERT accuracy to 25.68%, while VLESS Reality bypasses certificate-based detection entirely. We introduce AEGIS: an Adversarial Entropy-Guided Immune System powered by a Thermodynamic Variance-Guided Hyperbolic Liquid State Space Model (TVD-HL-SSM). Rather than competing in the Euclidean payload-reading domain, AEGIS discards payload bytes in favor of 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold. Liquid Time-Constants measure microsecond IAT decay, and a Thermodynamic Variance Detector computes sequence-wide Shannon Entropy to expose automated C2 tunnel anomalies. A pure C++ eBPF Harvester with zero-copy IPC bypasses the Python GIL, enabling a linear-time O(N) Mamba-3 core to process 64,000-packet swarms at line-rate. Evaluated on a 400GB, 4-tier adversarial corpus spanning backbone traffic, IoT botnets, zero-days, and proprietary VLESS Reality tunnels, AEGIS achieves an F1-score of 0.9952 and 99.50% True Positive Rate at 262 us inference latency on an RTX 4090, establishing a new state-of-the-art for physics-based adversarial network defense.