Skip to main content
SpringerLink
Log in

A Use Case Analysis of Heterogeneous Semistructured Objects in Information Security Problems

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract—

This paper is devoted to solving the problem of developing a case-based decision support system for information security problems. The source data can be described as heterogeneous semistructured objects and formalized as property vectors. An approach to constructing a knowledge base for such problems using a two-level representation (the level of case-objects and the use case structure level) is given. The authors consider a use case modeling method for preparing a basic data set. Methods for assessing the similarity of heterogeneous semistructured objects and higher-level use cases are proposed. Results of experimental approbation of the described solutions and the architecture of the corresponding decision support system are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Similar content being viewed by others

REFERENCES

  1. Dua, S. and Du, X., Data Mining and Machine Learning in Cybersecurity, Taylor and Francis Group, LLC, 2011.

    Book  MATH  Google Scholar 

  2. Lee, W., Stolfo, S.J. and Mok, K.W., Adaptive intrusion detection: A data mining approach, Artif. Intell. Rev., 2000, vol. 14, no. 6, pp. 533–567. doi 10.1023/A:1006624031083

    Article  MATH  Google Scholar 

  3. Dasgupta, D. and Gonzalez, F.A., An intelligent decision support system for intrusion detection and response, Information Assurance in Computer Networks. MMM-ACNS 2001; Lect. Notes Comput. Sci., 2001, vol 2052.

  4. Eremenko, N.N. and Kokoulin, A.N., Study of penetration testing methods in information systems, Master’s J., 2016, no. 2, pp. 181–186.

  5. Bertoglio, D.D. and Zorzo, A.F., Overview and open issues on penetration test, J. Braz. Comput. Soc., 2017, no. 23, p. 2.

  6. Tumanov, S.A., Means of penetration testing of information systems, Dokl. Tomsk. Gos. Univ. Sist. Upr. Radioelektron., 2015, no. 2, pp. 73–79.

  7. Zhukov, V.G. and Shalyapin, A.A., Precedent analysis of information security incidents, Sib. Zh. Nauki Tekhnol., 2013, no. 2.

  8. Richter, M.M. and Weber, R., Case-Based Reasoning, Springer-Verlag Berlin Heidelberg, 2013. https://doi.org/ 10.1007/978-3-642-40167-1

  9. Poltavtseva, M.A. and Pechenkin, A.I., Intelligent data analysis in decision support systems for penetration tests, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 985–991.

    Article  Google Scholar 

  10. Vasil’ev, V.I., Pestrikov, V.A., and Kras’ko, A.S., Intelligent support of decision-making in extreme situations based on the conclusion by precedents, Izv. Yuzhn. Fed. Univ., Tekh. Nauki, 2008, no. 8, pp. 7–14.

  11. Nikolaichuk, O.A., Knowledge modeling for studying the dynamics of the technical state of unique objects, Probl. Upr., 2009, no. 4, pp. 58–65.

  12. Desktop Operating System Market Share Worldwide. http://gs.statcounter.com/os-market-share/desktop/ worldwide. Accessed February 2, 2018.

  13. OS Platform Statistics. https://www.w3schools.com/browsers/browsers_os.asp. Accessed February 2, 2018.

  14. Market Share Statistics for Internet Technologies. Operating System Share by Version. https://netmarketshare.com/ operating-system-market-share.aspx. Accessed February 2, 2018.

  15. Security TechCenter. https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2017-0262. Accessed February 2, 2018.

  16. Security TechCenter. https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2017-0263. Accessed February 2, 2018.

  17. Neely, L., SANS Institute InfoSec Reading Room. Exploits at the Endpoint: SANS 2016 Threat Landscape Survey. https://www.sans.org/reading-room/whitepapers/analyst/exploits-endpoint-2016-threat-landscape-survey-37157. Accessed February 2, 2018.

  18. Thomas, H., International OpenOffice market shares. https://www.webmasterpro.de/portal/news/2010/ 02/05/international-openoffice-market-shares.html. Accessed February 2, 2018.

  19. Joachims, T., Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publ., 2002.

    Book  Google Scholar 

  20. Wallach, H.M., Topic modeling: Beyond bag-of-words, Proc. of the 23rd Int. Conf. on Machine Learning. ACM, 2006, pp. 977–984.

Download references

ACKNOWLEDGMENTS

This work was supported by the Ministry of Education and Science of the Russian Federation within the Federal Targeted Program “Research and Development of High-Priority Areas of Development of the Scientific–Technological Complex of Russia in 2014–2020,” project no. 14.578.21.0231 (RFMEFI57817X0231).

The results of the work were obtained using the computing resources of the Supercomputer Center Polytechnic of St. Petersburg Polytechnic University (http://www.spbstu.ru).

Author information

Authors and Affiliations

  1. St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    P. D. Zegzhda, M. A. Poltavtseva, A. I. Pechenkin, D. S. Lavrova & E. A. Zaitseva

Authors
  1. P. D. Zegzhda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Poltavtseva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. I. Pechenkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. S. Lavrova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. A. Zaitseva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to P. D. Zegzhda or M. A. Poltavtseva.

Additional information

Translated by O. Pismenov

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zegzhda, P.D., Poltavtseva, M.A., Pechenkin, A.I. et al. A Use Case Analysis of Heterogeneous Semistructured Objects in Information Security Problems. Aut. Control Comp. Sci. 52, 918–930 (2018). https://doi.org/10.3103/S0146411618080278

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411618080278

Keywords:

Search

Navigation