**6. Conclusions**

In this work, we proposed an integrated NN-based detection and model predictive control method for nonlinear process systems to account for potential cyber-attacks. The NN-based detection system was first developed with the sliding detection window to detect cyber-attacks. Based on that, the Lyapunov-based MPC was developed with the stability region check triggered by the detection indicator to achieve closed-loop stability in the sense that the closed-loop state remained within a well-characterized stability region and was ultimately driven to a small neighborhood around the origin. Finally, the proposed integrated NN-based detection and LMPC method was applied to a nonlinear chemical process example. The simulation results demonstrated that the min-max cyber-attack was successfully detected before the state exited the stability region, and the closed-loop system was stabilized under the LMPC by using the measurements from redundant secure sensors. The good performance of the proposed approach with respect to surge and replay cyber-attacks was also demonstrated. The value and importance of the NN-based detection method is twofold. First, the NN-based detection method is able to detect cyber-attacks without having to know the process model if a large amount of past data is available. This is very important as nowadays most SCADA systems are large-scale networks with complicated process models, while the big data processing becoming available in both storage and computation. Second, compared to other detection methods, the NN-based detection is easy to implement. The proposed detection and control method can improve the safeness of processes by effectively detecting known (or similar to known) cyber-attacks and also can be readily updated to handle new, unknown cyber-attacks. However, NN-based detection method also has its limitations. Although it achieves desired performance for a trained, known cyber-attack, it is not guaranteed to work for an unknown, new cyber-attack unless it shares similar properties with known cyber-attacks.

**Author Contributions:** Investigation, Z.W., F.A., J.Z., Z.Z. and H.D.; Methodology Z.W., F.A., J.Z., Z.Z. and H.D.; Writing, Z.W. and H.D.; Supervision, P.D.C.

**Funding:** Financial support from the National Science Foundation and the Department of Energy is gratefully acknowledged.

**Conflicts of Interest:** The authors declare that they have no conflict of interest regarding the publication of the research article.
