oa World Cybersecurity Indicator Using Computational Intelligence

The aim of this research is to investigate the utilisation of Computational Intelligence (CI) methods for constructing a World Cybersecurity Indicator (WCI) to enable consistent and transparent assessments of the cybersecu- rity capabilities of nation's through the utilisation of Synthetic Composite Indicators (SCI's) concept for ranking their readiness and progress. SCI are assessment tools usually constructed to evaluate and contrast entities perfor- mance by aggregating intangible measures in many areas such as technology and innovation. SCI key value is inhibited in its capacity to aggregate com- plex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing a CI approach to develop a new WCI. The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form a macro level cybersecurity indicator. To illustrate the method of construction a fully worked application is pre- sented. The application employs real variables of possible threats to the In- formation and Communication Technology (ICT). The weighting and aggre- gation results obtained were compared against classical approaches namely Principal Component Analysis, Factor Analysis and the Geometric Mean to weight and aggregate SCI's. The proposed model has the capability of weighting and aggregating major cybersecurity indicators into a single value that ranks nations even with limited data points. The validity and robustness of the WCI is evaluated using Monte Carlo simulation. In order to show the value added by the new cybersecurity index, the WCI is applied to the Middle East and North Africa (MENA) region as a special case study and then generalised. In total seventy-three countries were included, that are representative of developed, developing and under- developed nations. The nal and overall ranking results obtained, suggest novel and unbiased way compared to traditional or statistical methods when building, the WCI.