Cybersecurity: Identifying the Vulnerability Intensity Function (<i>VIF</i>) and Vulnerability Index Indicator (<i>VII</i>) of a Computer Operating System
- 1 Department of Mathematics and Statistics, University of South Florida, Tampa, USA
- 2 Department of Mathematics and Statistics, University of South Florida, Tampa, USA
Abstract
The objective of the present study is to define two important aspects of the computer operating system concerning the number of its vulnerabilities behavior. We identify the Vulnerability Intensity Function ( VIF ) , and the Vulnerability Index Indicator ( VII ) of a computer operating network. Both of these functions, VIF and VII are entities of the stochastic process that we have identi fied, which characterizes the probabilistic behavior of the number of vulnera bilities of a computer operating network. The VIF identifies the rate at which the number of vulnerabilities changes with respect to time. The VII is an important index indicator that conveys the following information about the number of vulnerabilities of Desktop Operating Systems: the numbers are increasing, de creasing, or remaining the same at a particular time of interest. This decision type of index indicator is crucial in every strategic planning and decision-making. The proposed VIF and VII illustrate their importance by using real data for Microsoft W indows Operating Systems 10, 8, 7, and Apple MacOS. The results of the actual data attest to the importance of VIF and VII in the cybersecurity problem we are currently facing.
- Desktop Operating System Market Share Worldwide. https://gs.statcounter.com/os-market-share/desktop/worldwide
- Kaluarachchi, P.K., Tsokos, C.P. and Rajasooriya, S.M. (2016) Cybersecurity: A Statistical Predictive Model for the Expected Path Length. Journal of Information Security, 7, 112-128. https://doi.org/10.4236/jis.2016.73008
- Rajasooriya, S.M., Tsokos, C.P. and Kaluarachchi, P.K. (2017) Cyber Security: Nonlinear Stochastic Models for Predicting the Exploitability. Journal of Information Security, 8, 125-140. https://doi.org/10.4236/jis.2017.82009
- Kaluarachchi, P., Tsokos, C. and Rajasooriya, S. (2018) Non-Homogeneous Stochastic Model for Cyber Security Predictions. Journal of Information Security, 9, 12-24. https://doi.org/10.4236/jis.2018.91002
- Pokhrel, N. and Tsokos, C. (2017) Cybersecurity: A Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process. Journal of Information Security, 8, 91-105. https://doi.org/10.4236/jis.2017.82007
- Alenezi, F. and Tsokos, C.P. (2020) Machine Learning Approach to Predict Computer Operating Systems Vulnerabilities. 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, 19-21 March 2020, 1-6. https://doi.org/10.1109/ICCAIS48893.2020.9096731
- Bain, L.J. and Engelhardt, M. (1991) Statistical Analysis of Reliability and Life-Testing Models. Marcel-Dekker, New York.
- Bassin, W.M. (1969) Increasing Hazard Functions and Overhaul Policy. Proceedings of the 1969 Annual Symposium on Reliability, Vol. 8, 173-178.
- Bozorgi, M., Saul, L.K., Savage, S. and Voelker, G.M. (2010) Beyond Heuristics: Learning to Classify Vulnerabilities and Predict Exploits. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC, 24-28 July 2010, 105-114. https://doi.org/10.1145/1835804.1835821
- Crow, L. (1974) Reliability Analysis for Complex Repairable Systems. In: Proschan, F. and Serfling, R.J., Eds., Reliability and Biometry, SIAM, Philadelphia, 379-410.
- Edkrantz, M. and Said, A. (2015) Predicting Cyber Vulnerability Exploits with Machine Learning. 13th Scandinavian Conference on Artificial Intelligence, Vol. 278, 48-57.
- Movahedi, Y., Cukier, M. and Gashi, I. (2019) Vulnerability Prediction Capability: A Comparison between Vulnerability Discovery Models and Neural Network Models. Computers & Security, 87, Article ID: 101596. https://doi.org/10.1016/j.cose.2019.101596