An AI‑Driven Risk Quantification Model for Industrial Internet of Things Devices
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| Publicat a: | ProQuest Dissertations and Theses (2026) |
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| Accés en línia: | Citation/Abstract Full Text - PDF |
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| Resum: | In recent years there has been a significant increase in the integration of Information Technology (IT) with Operational Technology (OT). As a result, there are now more vulnerabilities available to attackers than ever before within the Industrial Internet of Things (IIoT), and many of these vulnerabilities can be found at the critical infrastructure level. Unfortunately, most of the current methods of detecting cyberattacks such as rule-based Intrusion Detection Systems (IDS) and the use of manually monitored systems, are unable to quickly identify new types of attacks or provide immediate information on the scope of damage done during those attacks. This is why this research is developing and evaluating an Artificial Intelligence (AI) driven Risk Quantification Model for Modbus-based IIoT systems using supervised machine learning. In order to evaluate the performance of the Risk Quantification Model, a dataset called ToN_IoT Modbus was used, which is a dataset of network behaviors and device telemetry provided by the University of New South Wales. Raw data from both Network Behavior Data and Device Telemetry Data was analyzed to determine when abnormal behavior had occurred in the IIoT System. Once the data was transformed into a high-fidelity format for analysis, the data was normalized and timestamped for subsequent analysis. Stratified random sampling was then performed on the data to train and validate a Random Forest classifier that could categorize data as either normal or abnormal. The classifier achieved a detection accuracy of 94.5%, an F1 Score of 0.94 and an AUC ROC of 0.975, indicating that the Random Forest classifier achieved significantly higher accuracy for detecting abnormal behavior in the IIoT System compared to previous traditional rule-based Intrusion Detection Systems (IDS). Statistical testing of the results using t-tests and bootstrapping confirmed the validity and reliability of the results of this study. The results from this study support all three Research Hypotheses and show that the use of machine learning based risk quantification improves detection accuracy, reduces MTTR greater than 50% and improves classification performance of the IIoT system through the use of both network data and telemetry data. These empirical results suggest there is potential for future integration of AI-based models into industrial cybersecurity monitoring systems to provide an additional layer of defense against the growing number of IIoT cyber threats. |
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| ISBN: | 9798270212056 |
| Font: | Publicly Available Content Database |