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Related Experiment Videos

Security Audit of IoT Device Networks: A Reproducible Machine Learning Framework for Threat Detection and Performance

Aigul Shaikhanova1, Oleksandr Kuznetsov2,3, Aizhan Tokkuliyeva1

  • 1Department of Information Security, L.N. Gumilyov Eurasian National University, 2, Satpayeva St., Astana 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study presents a reproducible security audit framework for Internet of Things (IoT) networks, achieving high accuracy in detecting threats while ensuring computational efficiency and transparency for better IoT security assessments.

Keywords:
Internet of Things securitycybersecurityensemble learningintrusion detection systemsnetwork traffic analysissecurity audit

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning Applications

Background:

  • Internet of Things (IoT) deployments are increasingly vulnerable to security threats.
  • Existing intrusion detection evaluations prioritize algorithmic accuracy over operational needs like efficiency and reproducibility.
  • Systematic auditing methods for IoT network defense are underdeveloped.

Purpose of the Study:

  • To introduce a reproducible security audit framework for IoT device networks.
  • To evaluate the performance of machine learning models for IoT threat detection.
  • To provide actionable security recommendations based on audit findings.

Main Methods:

  • Developed a reproducible security audit framework for IoT networks.
  • Evaluated four machine learning models (Random Forest, LightGBM, XGBoost, Logistic Regression) on the TON_IoT dataset.
  • Excluded identity-revealing attributes for feature hygiene and benchmarked detection capability against computational cost.
  • Performed both binary and multiclass classification for threat detection and attribution.

Main Results:

  • Ensemble models achieved 99.8-99.9% accuracy in binary classification with 100% ROC-AUC and low inference latency.
  • Multiclass auditing reached 99.0% accuracy, but identified vulnerabilities in detecting rare attack types like man-in-the-middle (78% F1).
  • LightGBM demonstrated optimal performance, balancing high detection accuracy (99.93%) with a small deployment footprint (2.76 MB).

Conclusions:

  • The proposed framework delivers transparent, efficient, and reproducible security assessments for production IoT networks.
  • Actionable recommendations include network segmentation, rate-limiting, and TLS metadata collection.
  • The framework achieves competitive detection rates while addressing critical operational requirements for security audits.