Cybersecurity is rapidly gaining significance due to growing use of computers in daily life and business sectors. Likewise, industrial sector has also become more vulnerable to cyber threats (CT) exclusively with the onset of Industry 4.0, which is a digital transformation evolved with industrial control systems (ICS). Nowadays industrial organizations aim to build capacity towards protection of ICS to be cybersafe. To assess the effects of vulnerabilities in ICS, organizations utilize CVSS (Common Vulnerability Scoring System), which calculates severity categories/scores. CVSS is based on categorical variables defined by verbal statements rather than numerical values. When data collection is based on verbal/linguistic terms, uncertainty in data caused by human assessment inherently occurs. Randomness in data can be readily handled by classical statistical models, but to deal with uncertainty and especially when statistical assumptions for classical models don’t hold, fuzzy models with fuzzy numbers are appropriate to use. Therefore, we implement fuzzy logistic regression (FLR) on ICS vulnerability data, based on CVSS, to predict the severity category of ICS. Furthermore, the model is improved by applying metaheuristic algorithms to optimize the spread of fuzzy numbers representing input variables. This study is expected to contribute to practical application of vulnerability categorization of ICS.

This paper introduces a stochastic approach to case numbers of a pandemic disease. By defining the stochastic process random walk process is used. Some stochastic aspects for this disease are argued before stochastic study is started. During random walk process modeling new patients, recovering patients and dead conclusions are modelled and probabilities changes in some stages. Let the structure of this study includes vanishing process as a walk step, some wave happenings like big differences about spread speed as a big step in treatment- an effective vaccine or an influential chemical usage- a second corona virus pumping with virus mutation, a second global happening which bumping virus spread are defined as stages. This study only simulates a stochastic process of corona virus effects.