Development a mathematical model for the software security testing first stage

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.233417

Keywords:

software, security testing, graphic-analytical model, cyber threats, software safety, data protection

Abstract

This paper reports an analysis of the software (SW) safety testing techniques, as well as the models and methods for identifying vulnerabilities. An issue has been revealed related to the reasoned selection of modeling approaches at different stages of the software safety testing process and the identification of its vulnerabilities, which reduces the accuracy of the modeling results obtained. Two steps in the process of identifying software vulnerabilities have been identified. A mathematical model has been built for the process of preparing security testing, which differs from the known ones by a theoretically sound choice of the moment-generating functions when describing transitions from state to state. In addition, the mathematical model takes into consideration the capabilities and risks of the source code verification phase for cryptographic and other ways to protect data. These features generally improve the accuracy of modeling results and reduce input uncertainty in the second phase of software safety testing. An advanced security compliance algorithm has been developed, with a distinctive feature of the selection of laws and distribution parameters that describe individual state-to-state transitions for individual branches of Graphical Evaluation and Review Technique networks (GERT-networks). A GERT-network has been developed to prepare for security testing. A GERT-network for the process of checking the source code for cryptographic and other data protection methods has been developed. A graphic-analytical GERT model for the first phase of software safety testing has been developed. The expressions reported in this paper could be used to devise preliminary recommendations and possible ways to improve the effectiveness of software safety testing algorithms

Author Biographies

Serhii Semenov, National Technical University "Kharkiv Polytechnic Institute"

Doctor of Technical Sciences, Professor

Department of Computing and Programming

Zhang Liqiang, Neijiang Normal University

Postgraduate Student

College of Computer Science

Cao Weiling, Neijiang Normal University

Postgraduate Student

Department of IT Information Centre

Viacheslav Davydov, National Technical University "Kharkiv Polytechnic Institute"

PhD

Department of Computing and Programming

References

  1. Felderer, M., Büchler, M., Johns, M., Brucker, A. D., Breu, R., Pretschner, A. (2016). Security Testing: A Survey. Advances in Computers. Elsevier Ltd., 1–51. doi: http://doi.org/10.1016/bs.adcom.2015.11.003
  2. Felderer, M., Agreiter, B., Zech, P., Breu, R. (2011). A classification for model-based security testing. Advances in System Testing and Validation Lifecycle (VALID 2011), 109–114.
  3. El Far, I. K., Whittaker, J. A.; Marciniak, J. J. (Ed.) (2002). Model based software testing. Encyclopedia of Software Engineering. Wiley. doi: http://doi.org/10.1002/0471028959.sof207
  4. Atoum, I., Otoom, A. (2017). A Classification Scheme for Cybersecurity Models. International Journal of Security and Its Applications, 11 (1), 109–120. doi: http://doi.org/10.14257/ijsia.2017.11.1.10
  5. Dalalana Bertoglio, D., Zorzo, A. F. (2017). Overview and open issues on penetration test. Journal of the Brazilian Computer Society, 23 (1). doi: http://doi.org/10.1186/s13173-017-0051-1
  6. Minaev, V. A., Korolev, I. D., Mazin, A. V., Konovalenko, S. A. (2018). Model of vulnerability identification in unstable network interactions with automated system. Radio Industry, 2, 48–57. doi: http://doi.org/10.21778/2413-9599-2018-2-48-57
  7. Kostadinov, D. (2016). Introduction: Intelligence Gathering & Its Relationship to the Penetration Testing Process. Available at: https://resources.infosecinstitute.com/penetration-testing-intelligence-gathering
  8. Adebiyi, A., Arreymbi, J., Imafidon, C. (2013). A Neural Network Based Security Tool for Analyzing Software. Technological Innovation for the Internet of Things. Portugal, 80–87. doi: http://doi.org/10.1007/978-3-642-37291-9_9
  9. Semenov, S., Sira, O., Kuchuk, N. (2018). Development of graphic­analytical models for the software security testing algorithm. Eastern-European Journal of Enterprise Technologies, 2(4 (92)), 39–46. doi: http://doi.org/10.15587/1729-4061.2018.127210
  10. Semenov, S. G., Gavrylenko, S. Y., Chelak, V. V. (2016). Developing parametrical criterion for registering abnormal behavior in computer and telecommunication systems on the basis of economic tests. Actual Problems of Economics, 4 (178), 451–459.
  11. Yan, D., Liu, F., Jia, K. (2019). Modeling an information-based advanced persistent threat attack on the internal network. ICC 2019-2019 IEEE International Conference on Communications (ICC). Shanghai: IEEE. doi: http://doi.org/10.1109/icc.2019.8761077
  12. Tian-Yang, G., Yin-Sheng, S., You-Yuan, F. (2010). Research on software security testing. World Academy of science, engineering and Technology. International Journal of Computer and Information Engineering, 4 (9), 1446–1450.
  13. Semenov, S. H., Sur, O. O. (2012). Matematychna model systemy kryptohrafichnoho zakhystu elektronnykh povidomlen na osnovi GERT-merezhi. Systemy upravlinnia, navihatsiyi ta zviazku, 1 (1 (21)), 131–137.
  14. Dybach, A. M., Nosovskiy, A. V. (2015). Otsenka veroyatnosti prevysheniya kriteriev bezopasnosti. Yaderna ta radіatsіyna bezpeka, 4, 9–13. Available at: http://nbuv.gov.ua/UJRN/ydpb_2015_4_4
  15. Ango, A. (1964). Matematika dlya elektro- i radioinzhenerov. Moscow: Nauka, 772.

Downloads

Published

2021-06-30

How to Cite

Semenov, S. ., Liqiang, Z., Weiling, C., & Davydov, V. (2021). Development a mathematical model for the software security testing first stage. Eastern-European Journal of Enterprise Technologies, 3(2 (111), 24–34. https://doi.org/10.15587/1729-4061.2021.233417