pith:UZ4C23YT
Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices
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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Claims
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.
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.
Lightweight ML models enable 99% accurate intrusion detection on memory-limited IoT microcontrollers using decision trees and neural networks.
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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
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Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/UZ4C23YTALPT3SWYR2FJ55T4NL \
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# expect: a6782d6f1302df3dcad88e8a9ef67c6af219d99cb814d323b567297fde959622
Canonical record JSON
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