![]() ![]() Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. Past > Schools > School of Electrical Engineering & Computer ScienceĬonsult author(s) regarding copyright matters Past > QUT Faculties & Divisions > Science & Engineering Faculty Past > Institutes > Institute for Future Environments Experiments are conducted on a simplified chemical reactor control system to demonstrate the effectiveness of the presented approach.īayes methods, Heuristic algorithms, Risk management, cybersecurity, industrial control systems It is embedded with a noise evidence filter in order to reduce the impact from noise evidence caused by system faults. Then, an approximate dynamic inference algorithm is developed for dynamic assessment of ICS cybersecurity risk. To overcome the difficulty of limited historical data, the crisp probabilities used in standard Bayesian networks (BNs) are replaced in our approach by fuzzy probabilities. Firstly, an FPBN is established for analysis and prediction of the propagation of cybersecurity risks. In this paper, a fuzzy probability Bayesian network (FPBN) approach is presented for dynamic risk assessment. However, it is difficult to build a risk propagation model for ICSs due to the lack of sufficient historical data. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection. ![]() With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs.
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