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Ensuring Cyber Resilience of Large-Scale Network Infrastructure Using the Ant Algorithm

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Abstract

Application of the ant algorithm for ensuring the cyber resilience of a distributed system in conditions of various types of cyber attacks is considered. The principle of operation of the ant algorithm is described, a mathematical model of the network infrastructure is developed, and possible types of cyberattacks are determined within the framework of the model. The results of the experimental studies demonstrated the applicability of the ant algorithm for ensuring the cyber resilience of large-scale networks.

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REFERENCES

  1. Markov, Y.A., Kalinin, M.O., and Zegzhda, D.P., A technique of abnormal behavior detection with genetic sequences alignment algorithms, International Conference on Enterprise Information Systems and Web Technologies, EISWT 2010, 2010, pp. 104–110.

  2. Lavrova, D.S., Maintaining cyber sustainability in industrial systems based on the concept of molecular-genetic control systems, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 1026–1028.

    Article  Google Scholar 

  3. Zegzhda, P., Zegzhda, D., Kalinin, M., Pechenkin, A., Minin, A., and Lavrova, D., Safe integration of SIEM systems with Internet of Things: Data aggregation, integrity control, and bioinspired safe routing, ACM International Conference Proceeding Series, 2016, pp. 81–87. https://doi.org/10.1145/2947626.2947639

  4. Lavrova, D., Zaitceva, E., and Zegzhda, P., Bio-inspired approach to self-regulation for industrial dynamic network infrastructure, CEUR Workshop Proc., 2019, vol. 2603, pp. 34–39.

    Google Scholar 

  5. Benaicha, S.E., et al., Intrusion detection system using genetic algorithm, Science and Information Conference, London, 2014, pp. 564–568.

  6. Zou, Q., et al., Ant colony optimization algorithm in intrusion detection and positive, Appl. Mech. Mater., Trans Tech Publ., 2014, vol. 443, pp. 541–545.

    Google Scholar 

  7. Chen, H.H., et al., LDDoS attack detection by using ant colony optimization algorithms, J. Inf. Sci. Eng., 2016, vol. 32, no. 4, pp. 995–1020.

    Google Scholar 

  8. Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983.

    Article  Google Scholar 

  9. Krundyshev, V., Kalinin, M., and Zegzhda, P., Artificial swarm algorithm for VANET protection against routing attacks, 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018, 2018, pp. 795–800.

    Google Scholar 

  10. Kalinin, M., Demidov, R., and Zegzhda, P., Hybrid neural network model for protection of dynamic cyber infrastructure, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 4, pp. 375–382.

  11. Lavrova, D., Zegzhda, D., and Yarmak, A., Using GRU neural network for cyber-attack detection in automated process control systems, IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Sochi, 2019, pp. 1–3.

  12. Zegzhda, P., Zegzhda, D., Pavlenko, E., and Ignatev, G., Applying deep learning techniques for android malware detection, ACM International Conference Proceeding Series, 2018. https://doi.org/10.1145/3264437.3264476

  13. Belenko, V., Chernenko, V., Kalinin, M., and Krundyshev, V., Evaluation of GAN applicability for intrusion detection in self-organizing networks of cyber physical systems, 2018 International Russian Automation Conference, RusAutoCon, 2018. https://doi.org/10.1109/RUSAUTOCON.2018.8501783

  14. Krundyshev, V. and Kalinin, M., Hybrid neural network frame work for detection of cyber attacks at smart infrastructures, ACM International Conference Proceeding Series, 2019. https://doi.org/10.1145/3357613.3357623

  15. Ovasapyan, T.D., Moskvin, D.A., and Kalinin, M.O., Using neural networks to detect internal intruders in vanets, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 954–958.

    Article  Google Scholar 

  16. Yong, S.Z., Foo, M.Q., and Frazzoli, E., Robust and resilient estimation for cyber-physical systems under adversarial attacks, American Control Conference (ACC), Boston, MA, 2016, pp. 308–315.

  17. Thiede, S., Environmental sustainability of cyber physical production systems, Procedia CIRP, 2018, vol. 69, pp. 644–649.

    Article  Google Scholar 

  18. Zegzhda, D.P., Zegzhda, P.D., and Kalinin, M.O., Clarifying integrity control at the trusted information environment, Lect. Notes Comput. Sci., 2010, vol. 6258, pp. 337–344.

    Article  Google Scholar 

  19. Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321.

  20. Pavlenko, E.Y., Yarmak, A.V., and Moskvin, D.A., Hierarchical approach to analyzing security breaches in information systems, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 829–834.

    Article  Google Scholar 

  21. Zegzhda, D.P. and Pavlenko, E.Y., Cyber-sustainability of software-defined networks based on situational management, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 984–992.

    Article  Google Scholar 

  22. Stepanova, T., Pechenkin, A., and Lavrova, D., Ontology-based big data approach to automated penetration testing of large-scale heterogeneous systems, ACM International Conference Proceeding Series, 2015. https://doi.org/10.1145/2799979.2799995

  23. Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204.

  24. Dorigo, M., Optimization, learning and natural algorithms, PhD Thesis, Politecnico di Milano, 1992.

  25. Shtovba, S.D., Ant algorithms: Theory and application, Programmirovanie, 2005, vol. 31, no. 4, pp. 3–18.

    MATH  Google Scholar 

  26. Drigo, M., The ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. B, 1996, vol. 26, no. 1, pp. 1–13.

    Google Scholar 

  27. Dorigo, M., Bonabeau, E., and Theraulaz, G., Ant algorithms and stigmergy, Future Gener. Comput. Syst., 2000, vol. 16, no. 8, pp. 851–871.

    Article  Google Scholar 

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Funding

The research was carried out within the framework of scholarships of the President of the Russian Federation for young scientists and graduate students SP-1689.2019.5.

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Correspondence to E. Yu. Pavlenko.

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The authors declare that they have no conflicts of interest.

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Translated by K. Lazarev

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Pavlenko, E.Y., Kudinov, K.V. Ensuring Cyber Resilience of Large-Scale Network Infrastructure Using the Ant Algorithm. Aut. Control Comp. Sci. 54, 793–802 (2020). https://doi.org/10.3103/S0146411620080258

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  • DOI: https://doi.org/10.3103/S0146411620080258

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