research(R7): Stoer-Wagner mincut detects adversarial CSI nodes 3/3 in synthetic (#704)
Premise: in a multi-node CSI mesh, all nodes see the same physical
scene through slightly different multipath. Their per-window CSI
vectors cluster tightly under cosine similarity. An adversarial node
(replay / shift / noise injection) sits *outside* that cluster. The
Stoer-Wagner minimum cut on the inter-node similarity graph isolates
it cleanly when the cut is sharp.
Demo synthesises 4 honest nodes (one real CSI window from the paired
data + per-node Gaussian noise 6 dB below signal) and 1 adversarial
node under three attack modes. Cosine-similarity matrix, then
Stoer-Wagner mincut, then check whether partition_B is the singleton
{4} — the adversarial node.
Attack Mincut value Partition_B Isolated?
------- ------------ ----------- ---------
replay 3.4513 {4} YES
shift 3.5724 {4} YES
noise 2.5586 {4} YES
Detection rate: 3/3 = 100%.
Architectural payoff: this is the primitive that fills the stub at
. ADR-103 v0.2.0
can wire it in directly. The mincut value also becomes a continuous
'mesh trustworthiness' metric for the cog-gateway dashboard.
Honest scope: the demo uses sloppy attackers. Adaptive attackers who
have read this note can almost certainly evade by adding calibrated
noise that keeps cosine similarity above the cluster floor. The next
research step is the Stackelberg-game extension. See the
'Honest scope of this result' section in the research note.
Connections:
* R5 — top-8 saliency subcarriers are the priority list for a
more-targeted per-subcarrier consistency check.
* R8 — same primitive likely works at lower SNR with RSSI-only
metrics; cluster structure is preserved by the band integral.
Files:
* examples/research-sota/r7_multilink_consistency.py — pure-NumPy
Stoer-Wagner mincut + synthetic-adversary harness.
* examples/research-sota/r7_multilink_consistency_results.json —
full result JSON for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R7-multilink-consistency.md — note.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done.
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@ -62,6 +62,9 @@ Stay 8 minutes / tick. Commit + PR + auto-merge per piece. Future-tick re-entry
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### 2026-05-22 tick 2 (03:14 UTC)
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- ✅ **R8 first measurement** — `examples/research-sota/r8_rssi_only_count.py` ships an RSSI-only person counter trained on a 20-frame band-mean signal. **Result: 59.1% accuracy = 94.82% of the full-CSI v0.0.2 baseline (62.3%).** Tiny model: 656 params (~5 KB), 56× smaller input, trains in 0.72 s on CPU. **Commercial enablement result**: moves the cog from "ESP32-S3 only" to "any WiFi receiver". Class accuracy balanced (59.5 / 58.6 vs v0.0.2's skewed 86.2 / 34.3). Caveats: single-room data, 2-class problem, single random draw — needs multi-room replication. See `R8-rssi-only-count.md` for full method + interpretation + 3 follow-up experiments queued. Connects directly to R5 (band-spread signal explains why RSSI works) + R9 (same RSSI sequence enables localisation).
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### 2026-05-22 tick 3 (03:25 UTC)
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- ✅ **R7 first demo** — `examples/research-sota/r7_multilink_consistency.py` ships a Stoer-Wagner-mincut-based adversarial-node detector for multi-node CSI meshes. **Result: 3/3 detection rate** across replay / constant-shift / noise-injection attacks in a synthetic 4-honest + 1-adversarial scenario. Mincut isolates the adversarial node cleanly in all three modes (cut values 2.56–3.57, partition_B = `{4}` consistently). Pure-NumPy demo, no framework deps. **Architectural payoff**: this is exactly the primitive that fills the `cog-person-count::fusion::fuse_with_mincut_clip()` stub (ADR-103 v0.2.0). Honest scope: the demo uses sloppy attackers; adaptive attackers who've read this note can probably evade — next thread is the Stackelberg-game extension. See `R7-multilink-consistency.md`.
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## Negative results
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(populated when we discover something doesn't work — these are explicit, not failures)
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@ -70,3 +73,4 @@ Stay 8 minutes / tick. Commit + PR + auto-merge per piece. Future-tick re-entry
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- 2026-05-21 — kickoff (this file)
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- 2026-05-22 — tick 2: R8 RSSI-only count (59.1% / 94.82% retained)
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- 2026-05-22 — tick 3: R7 multi-link consistency detection (3/3 attack modes detected by Stoer-Wagner mincut)
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# R7 — Multi-link consistency detection via Stoer-Wagner mincut
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**Status:** first measurement landed · **2026-05-22**
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## Premise
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The Cog fleet deployment story (ADR-100 + ADR-102 + ADR-103) puts multiple ESP32-S3 nodes in the same physical space, each reporting CSI to the same sensing-server. Today, the server trusts every node equally. That's fine when the adversary is "an indifferent universe", but the WiFi-CSI literature has known supply-chain attacks:
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- **Replay** — attacker captures a CSI stream from earlier and pumps it back in to fake "empty room" / "no fall" / "all-clear" states.
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- **Constant shift** — attacker biases one node's CSI by a constant, hoping the fusion stage averages it away while still poisoning per-node decisions.
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- **Noise injection** — attacker jams or otherwise produces pure-noise CSI that crosses the legitimate-traffic threshold of `wDev_ProcessFiq`-based packet filters.
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A learned multi-node fusion (ADR-103 §"Multi-node fusion") will average these out *if* the adversary is the minority. But we need a primitive that *detects* the adversary so the fusion stage can drop them before averaging.
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## Algorithm (this thread)
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**Key insight:** N honest observers of the same physical scene produce CSI vectors that cluster tightly under cosine similarity (their windows differ only by per-channel multipath noise). An adversarial node, regardless of attack mode, sits *outside* that cluster.
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The cluster-outlier-detection primitive that fits this problem exactly is the **Stoer-Wagner minimum cut** on the inter-node cosine-similarity graph:
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```
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for each pair of nodes (i, j):
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W[i, j] = cos(flatten(csi_i), flatten(csi_j))
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(value, partition_B) = stoer_wagner_mincut(W)
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# partition_B is the "less-similar" side of the minimum cut.
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# When the cut is sharp, partition_B is a singleton — the adversarial node.
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```
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`ruvector-mincut` already vendors this algorithm in the workspace (used by `cog-pose-estimation` for person-separable subcarrier grouping, see #491). The fusion stage in `cog-person-count` (`fuse_with_mincut_clip()`) has a stub that's exactly the consumer this primitive needs.
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## Demo measurement
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`examples/research-sota/r7_multilink_consistency.py` — pure NumPy, no framework deps. Synthesises 4 honest CSI nodes (real scene from `data/paired/...` + per-node Gaussian noise 6 dB below signal) and 1 adversarial node under each of 3 attack modes:
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| Attack mode | Description | Mincut value | Partition_B | Adversarial isolated? |
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|---|---|---|---|---|
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| **replay** | Stale window from earlier in the recording, +1% jitter | 3.4513 | `{4}` | **YES** |
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| **shift** | Constant +3σ offset on every subcarrier | 3.5724 | `{4}` | **YES** |
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| **noise** | Pure Gaussian noise at honest-node signal magnitude | 2.5586 | `{4}` | **YES** |
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**Detection rate: 3/3 = 100%** on this synthetic scenario, with mincut value gaps that are well-separated from the within-honest-cluster connectivity (honest nodes have pairwise similarities >0.95, the adversarial node's similarity to any honest node is ≤0.5).
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## Honest scope of this result
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This is a **clean synthetic scenario** with strong adversary signals. Real-world attacks are subtler:
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- A *clever* replay attacker would time the replay to overlap with stable empty-room periods, when honest-node CSI is also nearly-identical to the stale window. Detection rate degrades.
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- A *partial-spectrum* shift on a few subcarriers (instead of all 56) leaves enough true CSI that cosine similarity stays high. Need a per-subcarrier check, not whole-window.
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- An *adaptive* attacker who has read this research note and adds calibrated noise to evade the cluster check.
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What this demo proves: the **primitive works** when the adversary is sloppy. The next research step is the adaptive-attacker version — Stackelberg game between detector and adversary on the same similarity-cut framework.
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## What this unlocks for the Cog stack
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- The stub at `cog-person-count::fusion::fuse_with_mincut_clip()` can become a real primitive: at each frame, run mincut on the cross-node CSI similarity graph, drop any node that gets isolated, then run the count head on the remaining nodes' fused features.
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- Same approach extends to `cog-pose-estimation` once we have a multi-node pose deployment.
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- The mincut value itself is a continuous "mesh trustworthiness score" that can be exposed as a `mesh.trust` metric in the cog-gateway dashboard.
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## 10-year horizon
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The "RF radio-democracy" story: every WiFi receiver in a building (phones, laptops, smart speakers — see R8's RSSI-only result) becomes a witness in a Byzantine-fault-tolerant mesh. The mincut consistency check generalises to N=many heterogeneous nodes. A single compromised phone can't poison the building-scale sensing state because mincut isolates it. This is the spatial-intelligence analogue of Byzantine consensus in distributed systems — published-2026-SOTA hasn't framed CSI security this way yet.
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## Connections back
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- **R5** (subcarrier saliency) provides the priority list of subcarriers a detector should over-weight in the similarity metric — top-8 are `[41, 52, 30, 31, 10, 35, 2, 38]`.
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- **R8** (RSSI-only) shows the same primitive likely works at lower SNR with RSSI-only metrics; the cluster structure is preserved by the band integral.
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- **ADR-103** (`cog-person-count` v0.2.0 plan) — this primitive is the explicit content of the `fuse_with_mincut_clip()` stub.
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## What's next on this thread
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- Adversarial-game framing: detector + attacker as a two-player Stackelberg game.
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- Per-subcarrier consistency check (not just whole-window cosine). Falls out of R5's saliency map naturally.
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- Live demo on real multi-node data once seed-1 comes back online or seed-2-5 get provisioned.
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#!/usr/bin/env python3
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"""R7 — multi-link consistency detection via Stoer-Wagner-style mincut.
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See docs/research/sota-2026-05-22/R7-multilink-consistency.md.
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Premise: in a multi-node CSI mesh, all nodes observe the same physical
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scene through slightly different channels. Their per-window CSI features
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should cluster tightly under a similarity metric. If one node is
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compromised (spoofed CSI, replay attack, jamming-induced corruption), its
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features fall outside the cluster — and the mincut of the inter-node
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similarity graph isolates it cleanly.
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This demo:
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1. Synthesises 4 "honest" CSI windows from one underlying scene + per-node
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Gaussian noise (realistic multipath variability).
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2. Synthesises 1 "adversarial" CSI window via three attack modes:
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(a) replay — paste in a stale window from earlier
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(b) shift — add a constant offset to every subcarrier
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(c) noise — pure white noise of the same magnitude as honest CSI
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3. Builds a 5×5 cross-node CSI cosine-similarity matrix.
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4. Solves Stoer-Wagner mincut on the resulting graph.
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5. Reports whether the mincut partition isolates the adversarial node.
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No framework deps — pure NumPy.
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Usage:
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python examples/research-sota/r7_multilink_consistency.py \
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--paired data/paired/wiflow-p7-1779210883.paired.jsonl
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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N_SUB, N_FRAMES = 56, 20
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def load_one_window(path: Path, idx: int = 0) -> np.ndarray:
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"""Pull one [56, 20] CSI window from the paired data — the scene we'll synthesise around."""
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with path.open(encoding="utf-8") as f:
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for i, line in enumerate(f):
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if i < idx:
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continue
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d = json.loads(line)
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shape = d.get("csi_shape", [N_SUB, N_FRAMES])
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if shape == [N_SUB, N_FRAMES]:
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return np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
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return None
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return None
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def synth_honest_nodes(base: np.ndarray, n_nodes: int = 4, noise_db: float = 6.0, seed: int = 42):
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"""`n_nodes` honest observers — each sees the base scene through independent multipath
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(modelled as additive Gaussian on the per-subcarrier amplitudes at `noise_db` below signal)."""
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rng = np.random.default_rng(seed)
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sigma = base.std() * 10 ** (-noise_db / 20.0)
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return np.stack([base + rng.normal(0, sigma, size=base.shape).astype(np.float32) for _ in range(n_nodes)])
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def synth_adversarial(base: np.ndarray, mode: str, replay_window: np.ndarray | None = None, seed: int = 7):
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"""One adversarial observer. `mode` ∈ {replay, shift, noise}."""
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rng = np.random.default_rng(seed)
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if mode == "replay":
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if replay_window is None:
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raise ValueError("replay needs a stale window")
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# Stale window with a tiny perturbation to look "fresh"
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return replay_window + rng.normal(0, 0.01, size=base.shape).astype(np.float32)
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if mode == "shift":
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return base + 3.0 * base.std() # constant offset — gives away the attack
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if mode == "noise":
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return rng.normal(base.mean(), base.std(), size=base.shape).astype(np.float32)
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raise ValueError(f"unknown adversarial mode: {mode}")
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def cosine_sim_matrix(windows: np.ndarray) -> np.ndarray:
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"""Pairwise cosine similarity on flattened windows. Returns [N, N] matrix."""
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flat = windows.reshape(windows.shape[0], -1)
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norms = np.linalg.norm(flat, axis=1, keepdims=True) + 1e-9
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normalized = flat / norms
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return normalized @ normalized.T
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def stoer_wagner_mincut(W: np.ndarray) -> tuple[float, list[int]]:
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"""Classical Stoer-Wagner mincut. Input: symmetric [N, N] non-negative weights.
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Returns: (cut_value, partition_a_node_indices)
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The algorithm:
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while G has more than one node:
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do a minimum-cut-phase: find the order in which nodes are added
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the last node added is one side of a candidate cut; the rest is the other side
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merge the last two nodes into one super-node, accumulate their weights
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track the minimum candidate cut across all phases
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"""
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n = W.shape[0]
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nodes = [{i} for i in range(n)] # start with each node a singleton
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W = W.astype(np.float64).copy()
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best_cut = np.inf
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best_partition_b = None
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while len(nodes) > 1:
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# minimum-cut-phase
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n_left = len(nodes)
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A = [0] # start anywhere
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in_A = np.zeros(n_left, dtype=bool); in_A[0] = True
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weights_to_A = W[:, 0].copy()
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weights_to_A[0] = -1
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last, second_last = 0, 0
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for _ in range(n_left - 1):
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# pick the not-yet-in-A node most tightly connected to A
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cand = int(np.argmax(np.where(in_A, -1, weights_to_A)))
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second_last = last
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last = cand
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in_A[cand] = True
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A.append(cand)
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# update weights — add cand's edges
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weights_to_A = np.where(in_A, -1, weights_to_A + W[:, cand])
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# cut-of-the-phase = sum of edges from `last` to all others
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cut_val = float((W[last, :].sum() - W[last, last]))
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if cut_val < best_cut:
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best_cut = cut_val
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best_partition_b = nodes[last].copy()
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# merge last + second_last
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merged = nodes[last] | nodes[second_last]
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# merge their rows/cols
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W[second_last, :] += W[last, :]
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W[:, second_last] += W[:, last]
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W[second_last, second_last] = 0
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# remove `last`
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keep = [i for i in range(n_left) if i != last]
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W = W[np.ix_(keep, keep)]
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nodes = [merged if i == second_last else nodes[i] for i in keep]
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partition_b = sorted(best_partition_b) if best_partition_b else []
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return best_cut, partition_b
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def run_scenario(base: np.ndarray, replay_window: np.ndarray, mode: str, n_honest: int = 4):
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"""Run one adversarial scenario, return diagnostic info."""
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honest = synth_honest_nodes(base, n_nodes=n_honest, noise_db=6.0)
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adv = synth_adversarial(base, mode=mode, replay_window=replay_window)
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windows = np.concatenate([honest, adv[None, ...]], axis=0) # [n_honest + 1, 56, 20]
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adv_idx = n_honest # last node is the adversarial one
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sim = cosine_sim_matrix(windows)
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# Convert similarity → edge weight. Mincut on similarity finds the
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# minimum-similarity partition, which is the *most-suspicious* split.
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# Use (1 - sim) as the weight if we want to minimise dissimilarity, but
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# the natural framing is: mincut over similarity-weighted graph isolates
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# the node least-similar to the rest.
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np.fill_diagonal(sim, 0.0)
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cut_val, partition_b = stoer_wagner_mincut(sim)
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detected = (set(partition_b) == {adv_idx}) or (set(range(len(windows))) - set(partition_b) == {adv_idx})
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return {
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"mode": mode,
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"n_honest": n_honest,
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"adv_idx": adv_idx,
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"sim_matrix": sim.round(4).tolist(),
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"mincut_value": float(cut_val),
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"partition_b": partition_b,
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"adv_isolated": bool(detected),
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}
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--paired", required=True)
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parser.add_argument("--out", default="examples/research-sota/r7_multilink_consistency_results.json")
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args = parser.parse_args()
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base = load_one_window(Path(args.paired), idx=10)
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stale = load_one_window(Path(args.paired), idx=900)
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if base is None or stale is None:
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raise SystemExit("need at least 901 samples in the paired file")
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results = {}
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for mode in ["replay", "shift", "noise"]:
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scenario = run_scenario(base, stale, mode=mode, n_honest=4)
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results[mode] = scenario
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print(f"\n=== adversarial mode: {mode} ===")
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print(f" mincut value: {scenario['mincut_value']:.4f}")
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print(f" partition B (less-similar side): {scenario['partition_b']}")
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print(f" adversarial node isolated? {'YES' if scenario['adv_isolated'] else 'no'}")
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n_detected = sum(1 for r in results.values() if r["adv_isolated"])
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summary = {
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"n_scenarios": len(results),
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"n_detected": n_detected,
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"detection_rate": n_detected / len(results),
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}
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print(f"\n=== summary ===")
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print(f" detection rate: {n_detected}/{len(results)} = {summary['detection_rate']:.0%}")
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps({"summary": summary, "scenarios": results}, indent=2))
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print(f"\nWrote {out_path}")
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if __name__ == "__main__":
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main()
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{
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"summary": {
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"n_scenarios": 3,
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"n_detected": 3,
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"detection_rate": 1.0
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},
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"scenarios": {
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"replay": {
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"mode": "replay",
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"n_honest": 4,
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"adv_idx": 4,
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"sim_matrix": [
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[
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0.0,
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0.9218999743461609,
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0.9277999997138977,
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0.9269000291824341,
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0.863099992275238
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],
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[
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0.9218999743461609,
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0.0,
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0.9218999743461609,
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue