Abstract The more critical infrastructures (CIs) being digitized, the more vulnerable they are regarding cyber security attacks. Digitisation-leveraging technologies in the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have been largely adopted for CIs, along with the Digital Twin (DT) paradigm. However, the distributed and heterogeneous nature of IoT or CPS poses significant challenges in safeguarding against diverse attack surfaces, including physical devices, network infrastructures, and third-party integration. To tackle these challenges, we propose an AI-driven DT-based security orchestration automation and response framework (SOAR4BC). Gathering system contexts from the DT in combination with security intelligence from the security tools gives us a holistic context for SOAR, which has not been seen in the existing approaches. We leverage this holistic context into the decision-making core, which utilizes advanced algorithms, like deep reinforcement learning, to generate adaptation recommendations based on incident alerts, risk assessments, and system state observations. By rigorously evaluating tampered data and distributed denial of service (DDoS) scenarios, we validate the SOAR4BC framework’s efficacy in handling security incidents leveraging digital twin environments. We further demonstrate real-world applicability through false-data injection and DoS attacks on an operational electric-vehicle charging testbed, confirming the practical effectiveness of SOAR4BC in securing critical infrastructures. Together, these results establish SOAR4BC as a robust and explainable AI-driven SOAR framework that advances the use of digital twins for cybersecurity in IoT and CPS ecosystems, offering actionable contributions for both research and industrial deployment.

Just-in-time defect prediction (JIT DP) leverages machine learning to identify defect-prone code commits, enabling quality assurance (QA) teams to allocate resources more efficiently by focusing on commits that are most likely to contain defects. Although JIT defect prediction techniques have introduced notable improvements in terms of predictive accuracy, they are still susceptible to misclassification errors such as false positives and false negatives. To preserve the practical utility of JIT defect prediction tools, it becomes essential to estimate the reliability of the predictions, i.e., computing confidence scores. Such scores can help practitioners identify predictions that are most likely to be correct. A simple approach to computing confidence scores is to extract, alongside each prediction, the corresponding prediction probabilities and use them as indicators of confidence. However, for these probabilities to reliably serve as confidence scores, the predictive model must be well-calibrated. This means that the prediction probabilities must accurately represent the true likelihood of each prediction being correct. Miscalibration, common in modern machine learning models, distorts probability scores such that the model’s prediction probabilities do not align with the actual probability of those predictions being correct. Despite its importance, model calibration has been largely overlooked in JIT defect prediction. In this study, we evaluate the calibration of several state-of-theart JIT defect prediction techniques to determine whether and to what extent they exhibit poor calibration. Furthermore, we assess whether post-calibration methods can improve the calibration of existing JIT defect prediction models. Our experimental analysis reveals that all evaluated JIT DP models exhibit some level of miscalibration, with Expected Calibration Error (ECE) ranging from 2% to 35%. Furthermore, post-calibration methods do not consistently improve the calibration of these JIT DP models. Index Terms-Just-in-time defect prediction, machine learning, model calibration, prediction probabilities, prediction reliability.