Intelligent support for detecting information security incidents

The relevance of the study is due to the need to automate the processes of detecting and identifying information security incidents for the timely launch of response processes, which, in turn, will reduce the impact of both intentional and accidental information security incidents on information security in automated systems for various purposes. The suggested solutions are based on artificial intelligence methods, and as a built-in means of intellectual support for the detection of information security incidents, a decision support system is employed. The article proposes models, mathematical dependencies and methods for solving problems of automatic detection, identification of information security incidents as well as their localization, for which, among other things, fuzzy set theory is used. Possible strategies for localizing information security incidents are considered. Procedures for responding to information security incidents as well as their elimination are formulated, which, in turn, allows building intelligent support systems for solving the problem of prompt detection of information security incidents. Examples of events are given. The materials of the article are of practical value for building systems of preventive information protection, which is currently one of the promising areas of theory and practice of ensuring information protection

1. Vasilyeva I.N. Investigation of information security incidents : a manual. Saint Petersburg: Publishing house of Saint Petersburg State University of Economics; 2019. 113 p. (In Russ.).

2. Manish G., Chandra B. A framework of intelligent decision support system for Indian police. Journal of Enterprise Information Management. 2014:27(5):512–540. DOI: http://dx.doi.org/10.1108/JEIM-10-2012-0073.

3. Jain G.P.-W. a. L. Recent Advances in Intelligent Decision Technologies. Lecture Notes in Computer Science. 2007:4692:567–571.

4. Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan, Kaufmann; 2005. 558 p.

5. Sudoplatov S.V., Ovchinnikova E.V. Mathematical logic and theory of algorithms. M.: «INFRA-M», 2004. 162 p. (In Russ.).

6. Sanzhez-Marre M., Gibert K. Evolution of Decision Support Systems. University of Catalunya; 2012. n. pag.

7. Luenberger D.G., Yinyu Ye. Linear and Nonlinear Programming. International Series in Operations Research & Management Science; 2021. n. pag.

8. Power D.J. Decision support systems: Concepts and resources for managers. Greenwood Publishing Group; 2002. n.pag.

9. Ltifi H., Trabelsi G., Ayed M., Alimi A. Dynamic Decision Support System Based on Bayesian Networks. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, 2012;1(1):22–29.

10. Burnside E.S., Rubin D.L., Fine J.P., Shachter R.D., Sisney G.A., Leung W.K. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology, 2006;240(3):666–673.