Cybersecurity Risk Analysis of Industrial Automation Systems on the Basis of Cognitive Modeling Technology

The issues of procuring the cybersecurity of modern industrial systems and networks acquire special urgency because of imperfection of their protection tools and presence of vulnerabilities. International standards ISA/IEC 62443 offer the system risk-oriented approach to solve the tasks of providing the security of industrial control systems (ICS) at all stages of life cycle. But in view of high uncertainty and complexity of procedure of formalizing the factors affecting the final indices of system security, the problem of cybersecurity risk assessment remains open and requires applying new approaches based on the technology of data mining and cognitive modeling. Cognitive modeling of risk assessment using fuzzy grey cognitive maps (FGCM) allows us to take into account the uncertainty factor arising in the process of vulnerability probability assessment for each of security nodes. The interval estimates of FGCM connection weights can reflect the scatter of expert group opinions that allows us to take into account more completely the data available for risk analysis. The main stages of ICS security assessment with use of FGCM are analyzed in the chapter on the example of distributed industrial automation network. The recommendations concerning the choice of the necessary countermeasures improving the level of network security in the conditions of possible external and internal threats are considered

On the treatment of uncertainty in innovation projects

Innovations encounter a relatively high level of uncertainty in their lifecycle path. As innovations are about implementing a new idea, they suffer from a shortage or lack of knowledge dependent on and directly proportional to the radical quality of novelty. They lack information to predict the future and face (high) uncertainty in the background knowledge used for the risk assessment. Incomplete information causes innovation risk analysts to assign subjective assumptions to simplify system models developed for innovation risk assessment. Subjective and non-subjective assumptions as uncertain assumptions are part of the background knowledge and source of uncertainty. This thesis tries to assess and treat innovation assumptions uncertainties by proposing a hybrid model which comprises the semi-quantitative risk assessment (SQRA) approach, extended semi-quantitative risk assessment (EQRA) approach, and knowledge dimension method. SQRA and EQRA highlight the criticality of assumptions and present a systematic approach to assess and treat assumption uncertainties. SQRA applies probabilistic analysis to conduct an assumptions risk assessment, and EQRA provides innovation managers with guidance on developing strategies to follow up uncertain assumptions over the process implementation. The knowledge dimension technique evaluates and communicates the strength of background knowledge applied in assumptions risk assessment to innovation decision-makers expressing whole uncertainty aspects in the background knowledge (assumptions, data, models, and expert judgment). The model can effectively contribute to innovation risks and uncertainties management during the project execution.2021-09-29T16:30:09