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.

翻译：由于TLS 1.3加密限制了传统的深度包检测（DPI），安全领域已转向基于欧几里得Transformer的分类器（例如ET-BERT）用于加密流量分析。然而，这些模型仍然容易受到字节级对抗性变形攻击——最近的预填充攻击将ET-BERT的准确率降至25.68%，而VLESS Reality则完全绕过了基于证书的检测。我们提出AEGIS：一种对抗性熵引导免疫系统，由热力学方差引导双曲液态状态空间模型（TVD-HL-SSM）驱动。与在欧几里得载荷读取领域竞争不同，AEGIS摒弃了载荷字节，转而采用投影到非欧几里得庞加莱流形中的6维连续时间流物理量。液态时间常数测量微秒级的IAT衰减，热力学方差检测器计算序列范围的香农熵，以暴露自动化的C2隧道异常。一个纯C++ eBPF采集器通过零拷贝IPC绕过Python GIL，使得线性时间O(N)的Mamba-3核心能够以线速率处理64,000个数据包集群。在包含骨干流量、物联网僵尸网络、零日漏洞和专有VLESS Reality隧道的400GB四层对抗性语料库上评估，AEGIS在RTX 4090上以262微秒推理延迟实现了0.9952的F1分数和99.50%的真正例率，为基于物理的对抗性网络防御树立了新的标杆。