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Weibull Mixture Models with Context-Specific Outliers for IoT Intrusion Detection

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  • Hassen Sallay

    (Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)

Abstract

We address here anomaly detection in the context of smart homes and the industrial internet of things (IIoT) by proposing a statistical framework based on Weibull mixture models (WMM) with context specific outlier handling. We model the normal traffic according to IoT device traffic characteristics. The outliers are modeled by uniform distributions for smart homes and Weibull distributions for the IIoT, reflecting their distinct attack profiles. We validated the proposed framework on both real and synthetic datasets for the specific context of the IIoT. WMM outperforms benchmark methods representing the common outlier detection approaches, achieving consistent robust ROC-AUC of 0.98 and Precison-AUC of 0.97. For its deployment in smart home and IIoT contexts, the framework provides adaptable architectural design to improve its efficiency implementation and management.

Suggested Citation

  • Hassen Sallay, 2026. "Weibull Mixture Models with Context-Specific Outliers for IoT Intrusion Detection," Future Internet, MDPI, vol. 18(8), pages 1-21, August.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:8:p:413-:d:2007478
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