{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T02:50:53Z","timestamp":1774579853684,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>The integration of blockchain technology into Cyber\u2013Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience.<\/jats:p>","DOI":"10.3390\/digital6010022","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:58:45Z","timestamp":1773046725000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Blockchain-Augmented CPS Framework to Mitigate FDI Attacks and Improve Resiliency"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8246-9221","authenticated-orcid":false,"given":"Mordecai Opoku","family":"Ohemeng","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Idaho, Moscow, ID 83844, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1241-2750","authenticated-orcid":false,"given":"Frederick T.","family":"Sheldon","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Idaho, Moscow, ID 83844, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Folgado, F.J., Calder\u00f3n, D., Gonz\u00e1lez, I., and Calder\u00f3n, A.J. (2024). Review of Industry 4.0 from the perspective of automation and supervision systems: Definitions, architectures and recent trends. Electronics, 13.","DOI":"10.3390\/electronics13040782"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"de S\u00e1, A.O., and Machado, R.C. (2020). Identification of Data Injection Attacks in Networked Control Systems with Varying Setpoint Condition. 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, IEEE.","DOI":"10.1109\/MetroInd4.0IoT48571.2020.9138299"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"de S\u00e1, A.O., Carmo, L.F.R.D.C., and Machado, R.C.S. (2019). Countermeasure for identification of controlled data injection attacks in networked control systems. 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT), IEEE.","DOI":"10.1109\/METROI4.2019.8792898"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111594","DOI":"10.1016\/j.automatica.2024.111594","article-title":"Emulation-based stabilization for networked control systems with stochastic channels","volume":"163","author":"Ren","year":"2024","journal-title":"Automatica"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101955","DOI":"10.1016\/j.cose.2020.101955","article-title":"False data injection attacks and the insider threat in smart systems","volume":"97","author":"Sayan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Konstantinou, C., and Maniatakos, M. (2016, January 24\u201328). A case study on implementing false data injection attacks against nonlinear state estimation. Proceedings of the 2nd ACM Workshop on Cyber-Physical Systems Security and Privacy, Vienna, Austria.","DOI":"10.1145\/2994487.2994491"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3944","DOI":"10.1038\/s41598-024-82566-6","article-title":"Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition","volume":"15","author":"Alrslani","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12411","DOI":"10.1109\/ACCESS.2024.3524942","article-title":"Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method","volume":"13","author":"Sahu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, J., Yang, X., and Yang, L.X. (2024). A framework for detecting false data injection attacks in large-scale wireless sensor networks. Sensors, 24.","DOI":"10.3390\/s24051643"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1016\/j.neucom.2017.10.009","article-title":"A survey on security control and attack detection for industrial cyber-physical systems","volume":"275","author":"Ding","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1109\/TAC.2020.2989773","article-title":"Switching-like event-triggered control for networked control systems under malicious denial of service attacks","volume":"65","author":"Peng","year":"2020","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18243","DOI":"10.1038\/s41598-025-03354-4","article-title":"Distributed denial-of-service (DDoS) on the smart grids based on VGG19 deep neural network and Harris Hawks optimization algorithm","volume":"15","author":"Alhashmi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ohemeng, M.O. (2025). Leveraging machine learning to predict bacterial population dynamics and address antimicrobial resistance. Qual. Quant., 1\u201326.","DOI":"10.1007\/s11135-025-02488-x"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100359","DOI":"10.1016\/j.ifacsc.2025.100359","article-title":"Enhancing autonomous vehicle control with lateral error feedback analysis","volume":"35","author":"Ohemeng","year":"2026","journal-title":"IFAC J. Syst. Control"},{"key":"ref_15","first-page":"1","article-title":"Ethereum: A secure decentralised generalised transaction ledger","volume":"151","author":"Wood","year":"2014","journal-title":"Ethereum Proj. Yellow Pap."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8287","DOI":"10.1109\/TII.2022.3142755","article-title":"A proof-of-authority blockchain-based distributed control system for islanded microgrids","volume":"18","author":"Yang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"28601","DOI":"10.1038\/s41598-025-13809-3","article-title":"Blockchain-based shop floor control system for small and medium-sized enterprise evolution to industry 4.0","volume":"15","author":"Lin","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4096","DOI":"10.1109\/TIE.2021.3071705","article-title":"Design of networked secure and real-time control based on blockchain techniques","volume":"69","author":"Yu","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TIE.2022.3148736","article-title":"Blockchain protocol-based predictive secure control for networked systems","volume":"70","author":"Yu","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alqahtani, A., Ohemeng, M.O., and Sheldon, F.T. (2026). An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning. Sensors, 26.","DOI":"10.3390\/s26030952"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yazdinejad, A., Karimipour, H., and Halabi, T. (2026). Towards Stress-Adaptive Cyber Defense: Cognitive\u2013Physiological Synchronization in IoT Environments. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2026.3656466"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.comcom.2023.03.017","article-title":"A blockchain-based data-driven fault-tolerant control system for smart factories in industry 4.0","volume":"204","author":"Masood","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101626","DOI":"10.1016\/j.phycom.2022.101626","article-title":"Security concerns on machine learning solutions for 6G networks in mmWave beam prediction","volume":"52","author":"Catak","year":"2022","journal-title":"Phys. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aich, N., Oubrahim, Z., Ait Talount, H., and Abbou, A. (2025). Bi-Scale Mahalanobis Detection for Reactive Jamming in UAV OFDM Links. Future Internet, 17.","DOI":"10.3390\/fi17100474"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Islambekov, U., Akcora, C., Smirnova, E., Gel, Y.R., and Kantarcioglu, M. (2020). Dissecting ethereum blockchain analytics: What we learn from topology and geometry of the ethereum graph?. Proceedings of the 2020 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9781611976236.59"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Teslya, N., and Ryabchikov, I. (2017). Blockchain-based platform architecture for industrial IoT. 2017 21st Conference of Open Innovations Association (FRUCT), IEEE.","DOI":"10.23919\/FRUCT.2017.8250199"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mylrea, M., and Gourisetti, S.N.G. (2017). Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security. 2017 Resilience Week (RWS), IEEE.","DOI":"10.1109\/RWEEK.2017.8088642"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1109\/TII.2019.2894573","article-title":"A blockchain-based solution for enhancing security and privacy in smart factory","volume":"15","author":"Wan","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TSG.2018.2819663","article-title":"Distributed blockchain-based data protection framework for modern power systems against cyber attacks","volume":"10","author":"Liang","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Putra, G.D., Dedeoglu, V., Kanhere, S.S., and Jurdak, R. (2022). Blockchain for trust and reputation management in cyber-physical systems. Handbook on Blockchain, Springer International Publishing.","DOI":"10.1007\/978-3-031-07535-3_10"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3309","DOI":"10.1109\/TAC.2023.3322379","article-title":"Filtering for nonlinear and linear Markov jump systems","volume":"69","author":"Costa","year":"2023","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/TAC.2023.3305345","article-title":"Asynchronous static output-feedback control of Markovian jump linear systems","volume":"69","author":"Tao","year":"2023","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jansch-Porto, J.P., Hu, B., and Dullerud, G.E. (2020). Convergence guarantees of policy optimization methods for Markovian jump linear systems. 2020 American Control Conference (ACC), IEEE.","DOI":"10.23919\/ACC45564.2020.9147571"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A plant-wide industrial process control problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput. Chem. Eng."},{"key":"ref_35","first-page":"129469","article-title":"Optimal tracking and regulation performance of networked control systems with additive colored Gaussian noise and finite bandwidth","volume":"501","author":"Zhang","year":"2025","journal-title":"Appl. Math. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bhowmick, C., and Jagannathan, S. (2020). Availability-Resilient Control of Uncertain Linear Stochastic Networked Control Systems. 2020 American Control Conference (ACC), IEEE.","DOI":"10.23919\/ACC45564.2020.9147499"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1007\/s12555-023-0633-y","article-title":"Performance limitations of network control systems under channel delay and packet dropouts","volume":"22","author":"Lu","year":"2024","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Decker, C., and Wattenhofer, R. (2013). Information propagation in the bitcoin network. IEEE P2P 2013 Proceedings, IEEE.","DOI":"10.1109\/P2P.2013.6688704"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Croman, K., Decker, C., Eyal, I., Gencer, A.E., Juels, A., Kosba, A., Miller, A., Saxena, P., Shi, E., and G\u00fcn Sirer, E. (2016). On Scaling Decentralized Blockchains: (A Position Paper). International Conference on Financial Cryptography and Data Security, Springer.","DOI":"10.1007\/978-3-662-53357-4_8"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gervais, A., Karame, G.O., W\u00fcst, K., Glykantzis, V., Ritzdorf, H., and Capkun, S. (2016, January 24\u201328). On the security and performance of proof of work blockchains. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria.","DOI":"10.1145\/2976749.2978341"},{"key":"ref_41","unstructured":"Buchman, E. (2016). Tendermint: Byzantine Fault Tolerance in the Age of Blockchains. [Ph.D. Thesis, University of Guelph]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kiffer, L., Rajaraman, R., and Shelat, A. (2018, January 15\u201319). A better method to analyze blockchain consistency. Proceedings of the 2018 Acm Sigsac Conference on Computer and Communications Security, Toronto, ON, Canada.","DOI":"10.1145\/3243734.3243814"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Thakkar, P., Nathan, S., and Viswanathan, B. (2018). Performance benchmarking and optimizing hyperledger fabric blockchain platform. 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), IEEE.","DOI":"10.1109\/MASCOTS.2018.00034"},{"key":"ref_44","first-page":"173","article-title":"Practical byzantine fault tolerance","volume":"Volume 99","author":"Castro","year":"1999","journal-title":"Proceedings of the Third Symposium on Operating Systems Design and Implementation"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, X., and Huang, D. (2025). Performance Analysis of Blockchain Consensus Algorithm in Unmanned Aerial Vehicle Ad Hoc Networks. Drones, 9.","DOI":"10.3390\/drones9050334"},{"key":"ref_46","first-page":"100065","article-title":"A survey on scalable consensus algorithms for blockchain technology","volume":"3","author":"Jain","year":"2025","journal-title":"Cyber Secur. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"43546","DOI":"10.1038\/s41598-025-27431-w","article-title":"Comparative evaluation and simulation of blockchain consensus mechanisms for secure and scalable peer to peer energy trading in microgrids","volume":"15","author":"Bhavana","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Do Costa, O.L.V., Marques, R.P., and Fragoso, M.D. (2005). Discrete-Time Markov Jump Linear Systems, Springer.","DOI":"10.1007\/b138575"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.automatica.2014.10.067","article-title":"A secure control framework for resource-limited adversaries","volume":"51","author":"Teixeira","year":"2015","journal-title":"Automatica"},{"key":"ref_50","unstructured":"Buterin, V. (2026, February 22). EIP 150: Gas Cost Changes for IO-Heavy Operations. Available online: https:\/\/github.com\/ethereum\/EIPs\/blob\/master\/EIPS\/eip-150.md."},{"key":"ref_51","unstructured":"Fisher, R. Iris [Dataset]. UCI Machine Learning Repository."}],"container-title":["Digital"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6470\/6\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:19:25Z","timestamp":1773206365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6470\/6\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,8]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["digital6010022"],"URL":"https:\/\/doi.org\/10.3390\/digital6010022","relation":{},"ISSN":["2673-6470"],"issn-type":[{"value":"2673-6470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,8]]}}}