Add nr-network-known-class-detector: v10 public-CVE cut (39 primitives, 9 chains, held-out ROC 0.9082)

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README.md

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+---
+license: apache-2.0
+library_name: scikit-learn
+tags:
+  - cybersecurity
+  - blockchain
+  - network-security
+  - validator-security
+  - anomaly-detection
+  - intrusion-detection
+  - ddos
+  - cve
+datasets:
+  - NullRabbit/nr-bundles-public
+metrics:
+  - roc_auc
+  - f1
+---
+
+# nr-network-known-class-detector
+
+A binary **attack-vs-benign** detector for **blockchain-node network/resource attacks**, trained
+entirely on faithful reproductions of **publicly-disclosed** attacks. Every attack class in the
+corpus reproduces a specific public disclosure — a CVE, a GHSA, or a named third-party security
+audit — and each carries a `provenance.source_class` recording how public its sourcing is. Part of
+NullRabbit's work on **autonomous defence for decentralised networks** — *watch the outside of the
+perimeter*.
+
+> **STATUS: DIAGNOSTIC, not a deployment claim.** Trained on synthetic localnet reproductions (lab
+> fidelity), not real production traffic. See *Evaluation* and *Limitations*.
+
+## Model description
+
+Given the network-layer signal of a short capture window against a blockchain node (packet-rate /
+size statistics from the pcap, and amplification / request-response / timing statistics from the RPC
+responses), the model emits a calibrated attack probability. It is one multi-family model over the
+`network-v1` feature manifold — it spans eight protocol layers and nine chains, all public-sourced
+(this published cut is trained on `public-cve-replication` primitives only).
+
+## Architecture
+
+- `HistGradientBoostingClassifier` + isotonic calibration (scikit-learn), NaN-native.
+- **34 features** kept (of the 103 `network-v1` features present in the corpus; 69 degenerate dropped
+  by a per-fit robust-column guard) — pcap aggregates + RPC-response aggregates. No host-load features
+  (the containerised lab node is root-owned, so CPU/connection host metrics are unreadable).
+- Decision threshold 0.5 (calibrated). Inference is **scoreability-gated**: a record with no network
+  signal (e.g. an economic/DeFi bundle) returns `scoreable=False` with no verdict.
+
+## Training data — 39 public-CVE attack primitives, 9 chains, 8 layers, 1084 bundles
+
+**This is the public-CVE cut** (`public-cve-replication` only): 708 attack + 376 benign bundles
+(`pcap + responses + manifest`), 45 chain×primitive instances. Benign traffic exercises the **same
+methods / wire messages** the attacks abuse, at normal scale — so the model separates attack-*use*
+from benign-*use*, not message type. Every attack reproduces an external public disclosure (CVE / GHSA
+/ named third-party audit) with a `provenance.public_source` URL. (An additional 8 `original`
+primitives — NullRabbit's own measurement of vendor-acknowledged Solana/Ethereum RPC amplification, for
+which no CVE exists — are held in the full corpus but **excluded from this published cut**, so the
+"trained entirely on public disclosures" claim above is literal.)
+
+| primitive | chain · layer | public source | source_class |
+|---|---|---|---|
+| `btc_inv_buffer_blowup` | Bitcoin · P2P | **CVE-2024-52915** | public-cve-replication |
+| `btc_invdos_flood` | Bitcoin · P2P | **CVE-2018-17145** (INVDoS) | public-cve-replication |
+| `btc_getdata_flood` | Bitcoin · P2P | **CVE-2024-52920** | public-cve-replication |
+| `btc_headers_oom` | Bitcoin · P2P | **CVE-2019-25220** | public-cve-replication |
+| `btc_orphan_cpu` | Bitcoin · P2P | **CVE-2024-52914** | public-cve-replication |
+| `btc_addr_overflow_flood` | Bitcoin · P2P | **CVE-2024-52919** / GHSA-qwp9-p9rr-h729 | public-cve-replication |
+| `btc_bloom_divzero` | Bitcoin · P2P | **CVE-2013-5700** | public-cve-replication |
+| `cosmos_protobuf_nest_bomb` | Cosmos · deserialization | **GHSA-8wcc-m6j2-qxvm** | public-cve-replication |
+| `sol_tpu_quic_handshake_flood` | Solana · TPU-QUIC | Neodyme Firedancer audit ND-FD04-LO-01 | public-cve-replication |
+| `geth_devp2p_ping_flood` | Ethereum · devp2p/RLPx | **CVE-2023-40591** (GHSA-ppjg-v974-84cm) | public-cve-replication |
+| `geth_rlpx_auth_flood` | Ethereum · devp2p/RLPx | **EL-2026-06** (EF public-disclosures) | public-cve-replication |
+| `gossipsub_prune_backoff_overflow` | libp2p · gossipsub | **CVE-2026-34219** / CVE-2026-33040 | public-cve-replication |
+| `gossipsub_subscribe_flood` | libp2p · gossipsub | **CVE-2026-46679** | public-cve-replication |
+| `libp2p_stream_exhaustion` | libp2p · muxer | **CVE-2022-23492** / CVE-2022-23486 | public-cve-replication |
+| `monero_levin_array_memcorrupt` | Monero · Levin/epee | **CVE-2018-3972** (CVSS 10) | public-cve-replication |
+| `monero_portable_storage_oom` | Monero · Levin/epee | Monero PR#7190 / 0.17.1.8 | public-cve-replication |
+| `btc_headers_genesis_spam` / `btc_inv_eviction_jam` / `btc_tx_quad_sighash` / `btc_oversized_recv_buffer` | Bitcoin · P2P | CVE-2024-52916 / -52913 / 2025-46598 / 2015-3641 | public-cve-replication |
+| `btc_version_timestamp_overflow` / `btc_version_selfnonce` | Bitcoin · P2P | CVE-2024-52912 / 2025-54604 | public-cve-replication |
+| `btc_cmpctblock_stall` / `btc_cmpctblock_overflow` | Bitcoin · P2P (BIP152) | **CVE-2024-52922** / **CVE-2025-46597** | public-cve-replication |
+| `btc_mutated_block` / `btc_invalid_block_logfill` / `btc_alert_flood` / `btc_tx_maprelay` | Bitcoin · P2P | CVE-2024-52921 / 2025-54605 / 2016-10724 / 2013-4627 | public-cve-replication |
+| `p2p_getheaders_drain` + inherited `btc_addr_overflow_flood` / `btc_orphan_cpu` | Bitcoin/Dogecoin/Litecoin · P2P | CVE-2023-33297 / 2024-52919 / 2024-52914 | public-cve-replication |
+| `geth_eth_receipt_flood` | Ethereum · devp2p/RLPx | **EL-2024-20** (EF public-disclosure) | public-cve-replication |
+| `geth_snap_trienode_dos` | Ethereum · devp2p/snap | **CVE-2021-41173** (GHSA-59hh-656j-3p7v) | public-cve-replication |
+| `geth_tcp_handshake_flood` | Ethereum · devp2p | **EL-2024-06** (EF public-disclosure) | public-cve-replication |
+| `sol_tpu_quic_slowloris` / `sol_tpu_quic_initial_cpu` | Solana · TPU-QUIC | Neodyme ND-FD04-IN-02 / ND-FD1-MD-02 | public-cve-replication |
+| `cosmos_p2p_conn_flood` | Cosmos · P2P | **CVE-2020-5303** (Tendermint) | public-cve-replication |
+| `libp2p_signed_peer_record_flood` | libp2p · identify | **CVE-2023-40583** | public-cve-replication |
+| `sui_verifier_hamsterwheel` / `sui_disassemble_panic` / `sui_move_recursion` | Sui · Move-VM / JSON-RPC | CertiK Skyfall ×2 / **CVE-2023-36184** | public-cve-replication |
+
+Distribution: **708** `public-cve-replication` attack bundles — **39 distinct primitives across 9
+chains** (Bitcoin, Ethereum, Solana, Sui, Cosmos, Monero, Dogecoin, Litecoin, libp2p) — plus **376**
+benign. This published cut contains **no `original` bundles**; the 8 `original` RPC-measurement
+primitives live in the full internal corpus and ship only if the operator explicitly opts in, always
+under their honest label.
+
+## Training procedure (methodology is the contribution)
+
+Per NullRabbit's pre-registration discipline: the corpus is built attack-by-attack from a public
+disclosure with `provenance.public_source`; a Cleanlab data-quality scan gates label-issues and
+duplicates before training; a methodology auditor reviews each gate event with sanity floors and
+falsification holdouts; honest limitations are stated; cycles — not the final number — are the
+contribution. This corpus passed audit **APPROVED WITH REFINEMENTS** (all applied) — including the
+correction of a benign train/test leak in one held-out eval, reported transparently.
+
+## Evaluation
+
+Diagnostic ML checks (the corpus of faithfully-modelled public attacks is the deliverable; these are
+secondary). Reproduced by `scripts/known_class_loco_eval.py` + `scripts/corpus_quality.py`.
+
+- **Corpus** (public-CVE cut): 1084/1084 distinct vectors, 0 duplicate rows; 2 Cleanlab review-flags — both deliberately-stealthy low-volume `gossipsub_subscribe_flood` captures that legitimately resemble benign (labels correct, not mislabels).
+- **Within-corpus held-out — GroupKFold by primitive (66 groups, leakage-clean): ROC 0.9082.** `corpus_sha256 known-class-v10-publiccve`.
+- **Leave-one-attack-primitive-out within Bitcoin (leak-clean, disjoint-benign):** all 20 Bitcoin primitives ≥ 0.969. Detection is on traffic *shape*, not deep wire-semantics.
+- **Leave-one-chain-out (HARD zero-shot transfer — *not* a deployment metric):** Cosmos / Ethereum / Litecoin 1.00, Dogecoin 0.995, Bitcoin 0.934, libp2p 0.844, Solana 0.688, Sui 0.661, **Monero 0.592**. Chains with few public-CVE primitives (Monero's unique Levin protocol; Sui/Solana with 3 each) have the fewest cross-chain near-neighbours, so zero-shot transfer to them is hardest — reported honestly, not spun.
+
+## Intended uses
+
+Research and benchmarking of network/resource-abuse detection on blockchain infrastructure; a
+worked, public-provenance reference corpus; downstream training. **Not** a turnkey production IDS.
+
+## Limitations
+
+- **Synthetic lab fidelity** — generated localnet traffic, not a real-world deployment claim. A
+  deployment claim needs a real-traffic validation gate (real mainnet RPC + real attack instances).
+- **Detection is on traffic *shape*** (volume / rate / size / connection-churn), not deep wire
+  semantics — adequate for these volumetric/crash DoS classes; it would not separate two attacks with
+  identical traffic profiles.
+- **No host-load features** (root-owned container).
+- **This is the public-CVE cut** — every shipped attack class reproduces an external public disclosure
+  (CVE / GHSA / named audit). The `original` Solana/Ethereum RPC-amplification measurements
+  (vendor-acknowledged but not CVE-backed — RPC amplification has ~no CVEs) are **excluded** from this
+  model; they exist in the full internal corpus and ship only on explicit operator opt-in.
+
+## How to use
+
+```python
+from predict import load, predict
+model = load("model.joblib")
+out = predict(model, [{"pcap.packet_rate": 850.0, "resp.amp_ratio_max": 224.0}])
+# -> [{"scoreable": True, "score": ..., "verdict": "attack"|"benign", "threshold": 0.5}]
+```
+
+Run `python inference_example.py` for a worked example on real captured vectors (Bitcoin + Solana
+attacks fire; benign Bitcoin peer is benign; an economic bundle is `scoreable=False`).
+
+## Licensing
+
+Apache-2.0 (see `LICENSE`). Attribution appreciated.
+
+## Citation
+
+```bibtex
+@software{nullrabbit_network_known_class_2026,
+  author = {NullRabbit Labs},
+  title  = {nr-network-known-class-detector: a public-provenance blockchain network-attack detector},
+  year   = {2026},
+  url    = {https://huggingface.co/NullRabbit/nr-network-known-class-detector}
+}
+```
+
+Related: the open **bundle format** (`nr-bundle-spec`), the **family taxonomy** (mechanism-defined),
+the **earned-autonomy framework** ([Zenodo 10.5281/zenodo.18406828](https://doi.org/10.5281/zenodo.18406828)),
+the NullRabbit substrate paper (in preparation), the dataset `NullRabbit/nr-bundles-public`, and
+[nullrabbit.ai](https://nullrabbit.ai).
+
+## Contact
+
+NullRabbit Labs — [huggingface.co/NullRabbit](https://huggingface.co/NullRabbit) · [nullrabbit.ai](https://nullrabbit.ai)

example_records.json

@@ -0,0 +1,95 @@
+[
+  {
+    "label": "bitcoin:btc_invdos_flood",
+    "expect": "attack",
+    "feat": {
+      "pcap.bytes_per_s": 79402.155,
+      "pcap.distinct_dst_ips": 1.0,
+      "pcap.distinct_src_dst_pairs": 295.0,
+      "pcap.distinct_src_ips": 1.0,
+      "pcap.duration_s": 7.679,
+      "pcap.mean_packet_size": 111.385,
+      "pcap.packet_count": 5474.0,
+      "pcap.packets_per_s": 712.862,
+      "pcap.rst_fraction": 0.056,
+      "pcap.syn_to_handshake_ratio": 0.402,
+      "pcap.tcp_fin": 1.0,
+      "pcap.tcp_handshake_ack": 1463.0,
+      "pcap.tcp_rst": 308.0,
+      "pcap.tcp_syn": 588.0,
+      "pcap.tcp_syn_ack": 294.0,
+      "pcap.tcp_total_packets": 5474.0,
+      "pcap.top_dst_port": 18444.0,
+      "pcap.top_dst_port_fraction": 0.484,
+      "pcap.top_src_port": 18444.0,
+      "pcap.top_src_port_fraction": 0.516,
+      "pcap.total_bytes": 609722.0,
+      "pcap.unique_dst_ports": 5.0,
+      "pcap.unique_src_ports": 5.0,
+      "resp.count": 0.0,
+      "resp.resp_bytes_total": 0.0
+    }
+  },
+  {
+    "label": "solana:sol_tpu_quic_initial_cpu",
+    "expect": "attack",
+    "feat": {
+      "pcap.bytes_per_s": 13828.406,
+      "pcap.distinct_dst_ips": 0.0,
+      "pcap.distinct_src_dst_pairs": 0.0,
+      "pcap.distinct_src_ips": 0.0,
+      "pcap.duration_s": 7.035,
+      "pcap.mean_packet_size": 256.0,
+      "pcap.packet_count": 380.0,
+      "pcap.packets_per_s": 54.017,
+      "pcap.rst_fraction": 0.0,
+      "pcap.syn_to_handshake_ratio": 0.0,
+      "pcap.tcp_fin": 0.0,
+      "pcap.tcp_handshake_ack": 0.0,
+      "pcap.tcp_rst": 0.0,
+      "pcap.tcp_syn": 0.0,
+      "pcap.tcp_syn_ack": 0.0,
+      "pcap.tcp_total_packets": 0.0,
+      "pcap.top_dst_port": 0.0,
+      "pcap.top_dst_port_fraction": 0.0,
+      "pcap.top_src_port": 0.0,
+      "pcap.top_src_port_fraction": 0.0,
+      "pcap.total_bytes": 97280.0,
+      "pcap.unique_dst_ports": 0.0,
+      "pcap.unique_src_ports": 0.0,
+      "resp.count": 0.0,
+      "resp.resp_bytes_total": 0.0
+    }
+  },
+  {
+    "label": "bitcoin:benign_bitcoin_mixed_normal",
+    "expect": "benign",
+    "feat": {
+      "pcap.bytes_per_s": 22167.878,
+      "pcap.distinct_dst_ips": 1.0,
+      "pcap.distinct_src_dst_pairs": 75.0,
+      "pcap.distinct_src_ips": 1.0,
+      "pcap.duration_s": 7.252,
+      "pcap.mean_packet_size": 112.416,
+      "pcap.packet_count": 1430.0,
+      "pcap.packets_per_s": 197.195,
+      "pcap.rst_fraction": 0.055,
+      "pcap.syn_to_handshake_ratio": 0.493,
+      "pcap.tcp_fin": 5.0,
+      "pcap.tcp_handshake_ack": 300.0,
+      "pcap.tcp_rst": 78.0,
+      "pcap.tcp_syn": 148.0,
+      "pcap.tcp_syn_ack": 74.0,
+      "pcap.tcp_total_packets": 1430.0,
+      "pcap.top_dst_port": 18444.0,
+      "pcap.top_dst_port_fraction": 0.517,
+      "pcap.top_src_port": 18444.0,
+      "pcap.top_src_port_fraction": 0.483,
+      "pcap.total_bytes": 160755.0,
+      "pcap.unique_dst_ports": 5.0,
+      "pcap.unique_src_ports": 5.0,
+      "resp.count": 0.0,
+      "resp.resp_bytes_total": 0.0
+    }
+  }
+]

inference_example.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+"""nr-network-known-class-detector — inference example (Apache-2.0).
+
+Runs real `network-v1` feature vectors (captured from the lab corpus and shipped in
+`example_records.json`) through the detector:
+  1. a Bitcoin Core P2P attack (INVDoS flood, CVE-2018-17145)      -> should FIRE
+  2. a Solana RPC attack (getProgramAccounts amplification)        -> should FIRE
+  3. a benign Bitcoin peer (same message types, normal rate)       -> benign
+  4. an out-of-domain record (no network signal)                   -> scoreable=False
+
+Run from the model repo dir:  python inference_example.py
+"""
+import json
+
+from predict import load, predict
+
+model = load("model.joblib")
+records = json.load(open("example_records.json"))
+
+# add an out-of-domain record (no pcap.*/resp.* signal) to show the scoreability gate
+records.append({"label": "out-of-domain (economic bundle)", "expect": "unscoreable",
+                "feat": {"econ.gov_weight_held_blocks": 0.0}})
+
+results = predict(model, [r["feat"] for r in records])
+for r, out in zip(records, results):
+    if out["scoreable"]:
+        flag = "✓" if out["verdict"] == r["expect"] else "✗"
+        print(f"{flag} {r['label']:<42} score={out['score']:<7} verdict={out['verdict']} "
+              f"(expect {r['expect']}, threshold {out['threshold']})")
+    else:
+        print(f"  {r['label']:<42} UNSCOREABLE (no network signal — out of domain)")

model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fb86002e090d9c42d96569c268385554dcc3eb5ac27c1626c22106b1cc22605
+size 3111901

predict.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""nr-network-known-class-detector — scoreability-gated inference helper (Apache-2.0).
+
+Self-contained: needs only numpy + joblib + scikit-learn (the version the model was trained with).
+Loads `model.joblib` (a dict carrying the HistGradientBoostingClassifier + its feature contract) and
+scores feature dicts produced by the NullRabbit `network-v1` featuriser (pcap + responses aggregates).
+
+SCOREABILITY GATE: this is a network/resource-abuse detector. A record is *scoreable* only if it
+carries at least one of the model's network features (pcap.* / resp.*) non-NaN. A record with no
+network signal (e.g. a pure economic/DeFi bundle, or an empty dict) is returned `scoreable=False`
+with no verdict — the model must not emit a confident score outside its domain.
+
+The model is DIAGNOSTIC (trained on synthetic localnet reproductions of public attacks); see the
+model card. Default decision threshold 0.5 (the classifier is isotonic-calibrated).
+
+Usage:
+    from predict import load, predict
+    model = load("model.joblib")
+    out = predict(model, [{"pcap.packet_rate": 850.0, "resp.amp_ratio_max": 224.0, ...}])
+    # -> [{"scoreable": True, "score": 0.99, "verdict": "attack", "threshold": 0.5}]
+"""
+from __future__ import annotations
+
+import joblib
+import numpy as np
+
+DEFAULT_THRESHOLD = 0.5
+
+
+def load(path: str = "model.joblib") -> dict:
+    m = joblib.load(path)
+    assert {"model", "feature_names"} <= set(m), "model.joblib is not the expected contract dict"
+    return m
+
+
+def _is_scoreable(feat: dict, names: list[str]) -> bool:
+    nameset = set(names)
+    for k, v in feat.items():
+        if k not in nameset or v is None:
+            continue
+        try:
+            if not np.isnan(float(v)):
+                return True
+        except (TypeError, ValueError):
+            continue
+    return False
+
+
+def predict(model: dict, records: list[dict], threshold: float = DEFAULT_THRESHOLD) -> list[dict]:
+    """Score a list of network-v1 feature dicts. Unscoreable records get no verdict.
+
+    `feature_names` in the contract is already the post-robust-guard set the model was fit on
+    (34 features); build the vector over exactly those, NaN for anything absent (HGB is NaN-native).
+    """
+    names = model["feature_names"]
+    clf = model["model"]
+    idx = {n: i for i, n in enumerate(names)}
+
+    out: list[dict | None] = []
+    rows, pos = [], []
+    for i, feat in enumerate(records):
+        if not _is_scoreable(feat, names):
+            out.append({"scoreable": False, "score": None, "verdict": None, "threshold": threshold})
+            continue
+        vec = np.full(len(names), np.nan)
+        for k, v in feat.items():
+            if k in idx and v is not None:
+                try:
+                    vec[idx[k]] = float(v)
+                except (TypeError, ValueError):
+                    pass
+        rows.append(vec)
+        pos.append(i)
+        out.append(None)
+
+    if rows:
+        proba = clf.predict_proba(np.array(rows))[:, 1]
+        for p_i, p in zip(pos, proba):
+            out[p_i] = {"scoreable": True, "score": round(float(p), 4),
+                        "verdict": "attack" if p >= threshold else "benign", "threshold": threshold}
+    return out
+
+
+if __name__ == "__main__":
+    import sys
+    m = load(sys.argv[1] if len(sys.argv) > 1 else "model.joblib")
+    print(f"loaded nr-network-known-class-detector: {len(m['feature_names'])} features, "
+          f"corpus {m.get('corpus_bundle_count')} bundles, version {m.get('features_version')}, "
+          f"sha {m.get('corpus_sha256')}")