用物理模型检测加密流量中的零日攻击，精度达99.5%。
AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection
- 将网络流量转为非欧几何空间的连续流体态，避开字节级对抗干扰
- 在400GB数据上实现99.5%准确率，单次推理仅262微秒
- 适合需要低延迟高精度的实时网络安全系统
随着TLS 1.3加密限制了传统深度包检测（DPI），安全领域转向基于欧几里得Transformer的分类器（如ET-BERT）分析加密流量。然而这些模型易受字节级对抗变形攻击——近期预填充攻击使ET-BERT准确率降至25.68%，而VLESS Reality可完全绕过证书检测。本文提出AEGIS：一种由热力学方差引导的双曲液态状态空间模型（TVD-HL-SSM）驱动的对抗熵引导免疫系统。AEGIS不依赖载荷字节，而是将6维连续时间流体物理投影至非欧双曲庞加莱流形。液态时间常数测量微秒级间隔衰减，热力学方差检测器计算序列整体香农熵以暴露自动化C2隧道异常。纯C++ eBPF采集器通过零拷贝IPC绕过Python GIL，使线性时间复杂度O(N)的Mamba-3核心可在线速率处理64,000包集群。在涵盖骨干网流量、IoT僵尸网络、零日漏洞及专有VLESS Reality隧道的400GB四层对抗语料上评估，AEGIS在RTX 4090上实现F1分数0.9952与99.50%真正例率，推理延迟仅262微秒，建立物理驱动对抗网络防御新标杆。
原文摘要 · Abstract (English)
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.
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