Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems

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Abstract
This paper investigates the integration of intrusion detection systems (IDSs) within Digital Twin (DT) architectures to enhance cybersecurity in industrial environments. Using the CICIDS2017, CIC Modbus, and 4SICS 2015 datasets, we evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) in detecting network intrusions. Results indicate that RF achieves an accuracy of 99.9% for CICIDS2017, with high precision, recall, and low false positives. In contrast, SVM exhibits an accuracy of 94.2% for the same dataset, struggling with high rates of false positives and moderate recall. Similarly, for 4SICS 2015, RF demonstrates an accuracy of 93%, being balanced and reliable for industrial applications, while SVM shows only 88% accuracy, with a low precision of 65% and a high false alarm rate. For the CIC Modbus dataset, RF displays an accuracy of 95% in validation and 93% in testing, highlighting strong detection in ICS networks. However, SVM maintains an accuracy of 88%, with weak separation between benign and malicious traffic, and a higher misclassification rate. Our findings highlight the importance of DT-IDS integration in real-time threat detection and system resilience, paving the way for future research in deep learning-based IDS solutions.
Funding Information
  • National Program for Research of the National Association of Technical Universities (GNAC ARUT 2023)

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