Empirical Study of the Stability of a Linear Filter Based on the Neyman–Pearson Criterion to Changes in the Average Values

1. Ovasapyan, T., Moskvin, D., and Tsvetkov, A., Detection of attacks on the Internet of Things based on intelligent analysis of devices functioning indicators, 13th Int. Conf. on Security of Information and Networks, Merkez, Turkey, 2020, Örs, B. and Elçi, A., Eds., New York: Association for Computing Machinery, 2020, p. 3. https://doi.org/10.1145/3433174.3433611

2. Krundyshev, V. and Kalinin, M., Prevention of false data injections in smart infrastructures, 2019 IEEE Int. Black Sea Conf. on Communications and Networking (BlackSeaCom), Sochi, 2019, IEEE, 2019, pp. 1–5. https://doi.org/10.1109/blackseacom.2019.8812786

3. Kalinin, M., Krundyshev, V., and Zegzhda, P., Cybersecurity risk assessment in smart city infrastructures, Machines, 2021, vol. 9, no. 4, p. 78. https://doi.org/10.3390/machines9040078

   Article  Google Scholar 

4. Kalinin, M.O. and Krundyshev, V.M., Analysis of a huge amount of network traffic based on quantum machine learning, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 1165–1174. https://doi.org/10.3103/s014641162108040x

   Article  Google Scholar 

5. Kalinin, M. and Krundyshev, V., Security intrusion detection using quantum machine learning techniques, J. Comput. Virol. Hacking Tech., 2022, vol. 19, no. 1, pp. 125–136. https://doi.org/10.1007/s11416-022-00435-0

   Article  Google Scholar 

6. Pavlenko, E.Yu., Yarmak, A.V., and Moskvin, D.A., Application of clustering methods for analyzing the security of Android applications, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 867–873. https://doi.org/10.3103/s0146411617080156

   Article  Google Scholar 

7. Marshev, I.I., Zhukovskii, E.V., and Aleksandrova, E.B., Protection against adversarial attacks on malware detectors using machine learning algorithms, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 1025–1028. https://doi.org/10.3103/s0146411621080198

   Article  Google Scholar 

8. 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. https://doi.org/10.3103/s0146411618080199

   Article  Google Scholar 

9. Ovasapyan, T.D., Knyazev, P.V., and Moskvin, D.A., Application of taint analysis to study the safety of software of the Internet of Things devices based on the ARM architecture, Autom. Control Comput. Sci., 2020, vol. 54, no. 8, pp. 834–840. https://doi.org/10.3103/s0146411620080246

   Article  Google Scholar 

10. Carlini, N. and Wagner, D., Towards evaluating the robustness of neural networks, 2017 IEEE Symp. on Security and Privacy (SP), San Jose, Calif., 2017, IEEE, 2017, pp. 39–57. https://doi.org/10.1109/sp.2017.49

11. Hendrycks, D. and Dietterich, T., Benchmarking neural network robustness to common corruptions and perturbations, 2019. https://doi.org/10.48550/arXiv.1903.12261

12. Bastani, O., Ioannou, Ya., Lampropoulos, L., Vytiniotis, D., Nori, A.V., and Criminisi, A., Measuring neural net robustness with constraints, Proc. 30th Int. Conf. on Neural Information Processing Systems, Barcelona, 2016, Lee, D.D., von Luxburg, U., Garnett, R., Sugiyama, M., and Guyon, I., Eds., Red Hook, N.Y.: Curran Associates, 2016, pp. 2621–2629.

13. Webb, S., Rainforth, T., Teh, Ye.W., and Kumar, M.P., A statistical approach to assessing neural network robustness, 2018. https://doi.org/10.48550/arXiv.1811.07209

14. Yu, F., Qin, Zh., Liu, Ch., Zhao, L., Wang, Ya., and Chen, X., Interpreting and evaluating neural network robustness, 2019. https://doi.org/10.48550/arXiv.1905.04270

15. Neyman, J. and Pearson, E.S., IX. On the problem of the most efficient tests of statistical hypotheses, Philos. Trans. R. Soc. London. Ser. A, 1933, vol. 231, nos. 694–706, pp. 289–337. https://doi.org/10.1098/rsta.1933.0009

16. Singh, A.K., Malicious and benign webpages dataset, Data Brief, 2020, vol. 32, p. 106304. https://doi.org/10.1016/j.dib.2020.106304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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