AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection | Route /signal-canvas/aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection

MCP example

{
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  "arguments": {
    "mode": "paper",
    "paper_ref": "aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection",
    "query_text": "Summarize AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection"
  }
}

source_context

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  "mode": "paper",
  "query": "AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection",
  "normalized_query": "2604.02149",
  "route": "/signal-canvas/aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection",
  "paper_ref": "aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

PDF: https://arxiv.org/pdf/2604.02149v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

Signal Canvas receipt window

Watch and verify: AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

/buildability/aegis-adversarial-entropy-guided-immune-system-thermodynamic-state-space-models-for-zero-day-network-evasion-detection

Watchwatch

Subject: AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

Verdict

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
AEGIS achieves an F1-score of 0.9952 and 99.50% True Positive Rate
Implicationpartial
Explicitly stated in the abstract with specific numeric results.
Verificationpartial
partial
Evidencepartial
enabling a linear-time O(N) Mamba-3 core to process 64,000-packet swarms at line-rate... at 262 us inference latency on an RTX 4090
Implicationpartial
Directly stated in the abstract with specific performance metrics.
Verificationpartial
partial
Evidencepartial
these models remain vulnerable to byte-level adversarial morphing -- recent pre-padding attacks reduced ET-BERT accuracy to 25.68%
Implicationpartial
Directly stated in the abstract as motivation for the work, with specific accuracy degradation cited.
Verificationpartial
partial
Evidencepartial
AEGIS discards payload bytes in favor of 6-dimensional continuous-time flow physics projected into a non-Euclidean Poincare manifold
Implicationpartial
Explicitly described as the core methodological approach in the abstract.
Verificationpartial
partial
Evidencepartial
a Thermodynamic Variance Detector computes sequence-wide Shannon Entropy to expose automated C2 tunnel anomalies
Implicationpartial
Directly stated as a key technical component of the system.
Verificationpartial
partial
Evidencepartial
A pure C++ eBPF Harvester with zero-copy IPC bypasses the Python GIL
Implicationpartial
Explicitly mentioned as an implementation detail supporting performance claims.
Verificationpartial
partial
Evidencepartial
Evaluated on a 400GB, 4-tier adversarial corpus spanning backbone traffic, IoT botnets, zero-days, and proprietary VLESS Reality tunnels
Implicationpartial
Directly stated evaluation methodology with specific dataset characteristics.
Verificationpartial
partial
Evidencepartial
VLESS Reality bypasses certificate-based detection entirely
Implicationpartial
Stated as motivation for the work, though not quantified beyond the bypass claim.
Verificationpartial
partial

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AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection

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