AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index 


Vol. 49,  No. 2, pp. 321-331, Feb.  2024
10.7840/kics.2024.49.2.321


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  Abstract

In Industrial Internet of Things (IIoT) environments, the reliability and adaptability of machine learning models are crucial for accurate decision-making. This paper introduces the Characteristic Stability Index (CSI) to monitor and ensure the stability of models in the context of heterogeneous IIoT sensor data. The CSI quantifies the variations in feature importance rankings, enabling the early detection of data drift and shifts. The experimentation results validate the performance of the decision tree algorithm to provide actionable insights, facilitating domain experts’ adaptability and enhancing decision-making while minimizing operational risks and costs in the choice of intrusion detection systems model.

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[IEEE Style]

L. A. C. Ahakonye, C. I. Nwakanma, J. M. Lee, D. Kim, "AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 321-331, 2024. DOI: 10.7840/kics.2024.49.2.321.

[ACM Style]

Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, and Dong-Seong Kim. 2024. AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index. The Journal of Korean Institute of Communications and Information Sciences, 49, 2, (2024), 321-331. DOI: 10.7840/kics.2024.49.2.321.

[KICS Style]

Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, Dong-Seong Kim, "AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 321-331, 2. 2024. (https://doi.org/10.7840/kics.2024.49.2.321)
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