Modern technology integrates Cyber-Physical Systems (CPS), merging computational and physical processes. Ensuring CPS dependability is vital in averting adverse effects on critical applications due to unforeseen behaviour. To fortify CPS resilience, a novel technique for dynamic behavioural prediction and fault injection is introduced. It predicts dynamic CPS behaviour through system modelling under diverse operational scenarios, employing a fault model with diverse fault classes. Unlike the single model tenet, this approach engages multiple expert models to simulate both faultless and faulty behaviours. By adopting this approach, we can inject specialised faults and scale the analysis of the faults together or separately. Injecting faults assesses system reactions and reveals vulnerabilities. Tested on a water tank system, the approach proves effective in behaviour prediction and proactive fault handling, enhancing CPS design for robust, secure, and fault-tolerant systems.

Cyber Physical Systems (CPS) pose a pressing need to ensure they are sufficiently reliable and continue to be dependable. It is, therefore, essential to test these systems to uncover any potential anomalies, which if not detected can lead to failure and/or cause loss or injury. Adequate or complete coverage of behaviours can be difficult to accomplish in CPS. We advocate a less expensive and easy-to-evaluate representation of the system via surrogate modelling. In this paper, we present a novel predictive fault modelling framework leveraging surrogate-based Digital Twin for probing for likely faults that can support software analysts and testers of CPS in their testing plans. The approach abstracts the CPS and uses a variant of Recurrent Neural Network known as Long Short-Term Memory (LSTM) surrogate model for forecasting. The forecasting can help in predicting multiple behaviours of the system components and the likely faults of systems under test; observations will consequently feed into the testing plans. Both direct and iterative (i.e. one-time and multiple-time varying steps) forecasting are supported as part of the framework. We evaluate our surrogate-based Digital Twins predictive modelling approach on two CPSs namely: water distribution system and air pollution detection system. The results show that our approach performed decently in predicting multiple time steps.