Pith Number

pith:CUM4LKVH

pith:2026:CUM4LKVHI55QXZFTTTRGCKGZV4

not attested not anchored not stored refs resolved

Unsupervised Baseline Clustering and Incremental Adaptation for IoT Device Traffic Profiling

John D. Hastings, Sean M. Alderman

Density-based clustering best matches ground-truth IoT device labels in unsupervised traffic profiling while incremental methods trade purity for adaptability.

arxiv:2602.24047 v1 · 2026-02-27 · cs.NI · cs.CR · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{CUM4LKVHI55QXZFTTTRGCKGZV4}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

density-based clustering (DBSCAN) isolates a substantial outlier portion of the data and produces the strongest alignment with ground-truth device labels among tested classical methods (NMI 0.78)

C2weakest assumption

The selected long-duration captures from the Deakin IoT dataset are representative of real-world evolving IoT traffic and that flow features alone suffice to distinguish device identities across time.

C3one line summary

DBSCAN on flow features reaches NMI 0.78 with ground-truth IoT device labels on Deakin captures, while BIRCH supports 0.13-second incremental updates with 0.87 purity on a novel device.

References

19 extracted · 19 resolved · 0 Pith anchors

[1] A machine learning based framework for IoT device identification and abnormal traffic detection, 2022 · doi:10.1002/ett.3743

[2] A Generic Machine Learning Approach for IoT Device Identifica- tion, 2021 · doi:10.1109/iccws53234.2021.9702983

[3] Machine Learning With Computer Networks: Tech- niques, Datasets, and Models, 2024 · doi:10.1109/access.2024.3384460

[4] In 2024 International Conference on Computing, Networking and Communications (ICNC) 2024 · doi:10.1109/icnc59896.2024.10556143

[5] IoTTFID: An incremental IoT device iden- tification model based on traffic fingerprint, 2023 · doi:10.1109/access.2023.3284542

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-02T03:04:39.462037Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1519c5aaa7477b0be4b39ce26128d9af1145785fb77563d10518ff744f2f826c

Aliases

arxiv: 2602.24047 · arxiv_version: 2602.24047v1 · doi: 10.48550/arxiv.2602.24047 · pith_short_12: CUM4LKVHI55Q · pith_short_16: CUM4LKVHI55QXZFT · pith_short_8: CUM4LKVH

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CUM4LKVHI55QXZFTTTRGCKGZV4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1519c5aaa7477b0be4b39ce26128d9af1145785fb77563d10518ff744f2f826c

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "118afef012ca1557e8e6a741ef0c2e510859eb2ff5fab31552c75e428fac39cb",
    "cross_cats_sorted": [
      "cs.CR",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.NI",
    "submitted_at": "2026-02-27T14:31:01Z",
    "title_canon_sha256": "ffdc9cfce37752eaa817ba583d442420774b777659aa351bf8e604cd9224f87a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.24047",
    "kind": "arxiv",
    "version": 1
  }
}