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pith:UZ4C23YT

pith:2026:UZ4C23YTALPT3SWYR2FJ55T4NL
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Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices

Aftab Khan, Eirini Anthi, Pete Burnap, Pietro Carnelli, Theodoros Spyridopoulos, Vasilis Ieropoulos

Lightweight machine learning models detect intrusions on IoT microcontrollers with up to 99 percent accuracy.

arxiv:2605.13159 v1 · 2026-05-13 · cs.CR

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3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

Our study introduces a lightweight model that utilises machine learning algorithms to achieve a notable detection accuracy of 99% using a decision tree method and 96% using a neural network in identifying cyber threats, including Denial of Service and Man-in-the-Middle attacks which make up the majority of the attacks these devices face.

C2weakest assumption

The assumption that the proposed models can be effectively deployed and run in real-time on actual resource-constrained microcontrollers without exceeding memory or computational limits, and that the reported accuracies generalize beyond the tested scenarios to real-world threats.

C3one line summary

Lightweight ML models enable 99% accurate intrusion detection on memory-limited IoT microcontrollers using decision trees and neural networks.

References

61 extracted · 61 resolved · 0 Pith anchors

[1] A critical review of intrusion detection systems in the internet of things: Techniques, deployment strategy, val- idation strategy, attacks, public datasets and challenges, 2021
[2] The anatomy of security microcontrollers for iot appli- cations, 2020
[3] European Parliament and Council, “Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal 2016
[4] Study of network ids in iot devices, 2023
[5] e. espressif, “Esp32-s3,” Dec 2020. [Online]. Available: https: //www.espressif.com/en/products/socs/esp32-s3 2020

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First computed 2026-05-18T03:08:56.914913Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

a6782d6f1302df3dcad88e8a9ef67c6af219d99cb814d323b567297fde959622

Aliases

arxiv: 2605.13159 · arxiv_version: 2605.13159v1 · doi: 10.48550/arxiv.2605.13159 · pith_short_12: UZ4C23YTALPT · pith_short_16: UZ4C23YTALPT3SWY · pith_short_8: UZ4C23YT
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/UZ4C23YTALPT3SWYR2FJ55T4NL \
  | 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: a6782d6f1302df3dcad88e8a9ef67c6af219d99cb814d323b567297fde959622
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
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