Pith Number

pith:PP3CJZ3F

pith:2026:PP3CJZ3FP2QOFG6Q7MDMZE7PUD

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Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data

Abdulrahman Albaiz, Fathi Amsaad

A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources.

arxiv:2604.08581 v1 · 2026-03-28 · cs.LG

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\pithnumber{PP3CJZ3FP2QOFG6Q7MDMZE7PUD}

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Claims

C1strongest claim

Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash.

C2weakest assumption

That power side-channel RMS values under controlled anomaly conditions in the 14-day mini-fridge dataset are representative of real-world anomalies and that Z-score thresholds derived from the training phase will generalize without overfitting or missing subtle deviations.

C3one line summary

A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.

References

15 extracted · 15 resolved · 0 Pith anchors

[1] A Comprehensive Survey on TinyML 2023

[2] TinyML -Enabled Frugal Smart Objects: Challenges and Opportunities 2020

[3] Benchmarking TinyML Systems: Challenges and Direction 2003

[4] Anomaly Detection in Smart Environments: A Comprehensive Survey 2024

[5] Nonintrusive Appliance Load Monitoring: Review and Outlook 2011

Receipt and verification

First computed	2026-06-12T01:09:27.558787Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358

Aliases

arxiv: 2604.08581 · arxiv_version: 2604.08581v1 · doi: 10.48550/arxiv.2604.08581 · pith_short_12: PP3CJZ3FP2QO · pith_short_16: PP3CJZ3FP2QOFG6Q · pith_short_8: PP3CJZ3F

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD \
  | 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: 7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358

Canonical record JSON

{
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    "abstract_canon_sha256": "e80e9c38142e9ff7899e66cf87ab20aeaf86a1ef5ae795a8ec765155fff7621f",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-03-28T20:15:50Z",
    "title_canon_sha256": "cf92076f4c65e72a79ad833d692fb3d80fb87fea233987d56bd26871b21ec01f"
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    "kind": "arxiv",
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}