Intelligent Security Model for Digital Twins: An Autoencoder-Based Anomaly Detection Framework

Abstract

The extensive use of Digital Twin (DT) technologies in the field across many industries has brought substantial benefits of real-time monitoring, predictive maintenance, and operational efficiency. The extensive integration of physical assets with their digital twins is accompanied by severe cybersecurity threats. Rule-based security systems are normally challenged to adapt with dynamic behavioral patterns and fail to identify complex and subtle anomalies. Overcoming these shortcomings, this research puts forth a smart security framework tailored for detecting anomalies in DT environments through deep learning techniques. The primary mission is to enhance threat detection precision while enabling adaptive countermeasures to evolve with rapidly changing cyber threats. The strategy employs an unsupervised autoencoder neural network that identifies compact latent representations of normal DT system behavior. Anomalies are identified by quantitatively measuring the reconstruction error between input data and output data. The system is structured into four functional layers: Data Preprocessing, Autoencoder-Based Anomaly Detection, Adaptive Security Updating, and Incident Response. Dynamic thresholding mechanisms allow real-time anomaly classification, and feedback loops allow retraining at fixed intervals to maintain effectiveness in a continuously changing environment. Experimental evaluation with synthetic and real DT datasets demonstrated the high performance of the model with accuracy of 92%, recall of 94.5%, F1-score of 93.2%, and ROC AUC of 97%, as compared to comparative baselines such as Isolation Forest and One-Class SVM. Overall, the autoencoder-based system here provides a promising, scalable, and adaptive approach for safeguarding modern-day cyber-physical systems through real-time smart anomaly detection.