doi: 0.17586/2226-1494-2020-20-5-770-772


RISK IDENTIFICATION OF SECURITY INFORMATION VIOLATIONS IN CYBER-PHYSICAL SYSTEMS BASED ON ANALYSIS OF DIGITAL SIGNALS

V. V. Semenov, S. A. Arustamov


Read the full article  ';
Article in Russian

For citation:
Semenov V.V., Arustamov S.A. Risk identification of security information violations in cyber-physical systems based on analysis of digital signals. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 5, pp. 770–772 (in Russian). doi: 10.17586/2226-1494-2020-20-5-770-772


Abstract
Subject of Research. The paper presents an approach to the analysis of digital signal sequences related to cyber-physical systems functioning. The proposed solution combines a set of machine learning methods for analyzing heterogeneous external data of digital signals coming from various system sensors. Method. The methods based on artificial neural networks and the k-nearest neighbors algorithm were studied for the analysis of digital signals. Main Results. The proposed approach has been tested using the signals received from a digital three-axis accelerometer located on an unmanned vehicle prototype. The processing of digital signals by the methods under study has been carried out in the MATLAB R2020a environment. The accuracy of the researched methods has been compared and, as a result, the k-nearest neighbors algorithm reached the value of 96.1 %, whereas artificial neural networks showed the result of 95.0 %. Practical Relevance. The proposed approach makes it possible to detect the risks of information security violations of the cyber-physical systems with acceptable accuracy and can be used in systems for the state monitoring of objects.

Keywords: information security, cyber-physical systems, risk identification, signal analysis, monitoring systems

Acknowledgements. The paper was prepared with the financial support of the Ministry of Science and Higher Education of the Russian Federation under the agreement No. 075-15-2019-1707 dated from 22.11.2019 (identifier RFMEFI60519X0189, internal number 05.605.21.0189).

References
1. Zegzhda D.P., Pavlenko E.Y. Homeostatic security of cyber-physical systems. Information Security Problems. Computer Systems, 2017, no. 3, pp. 9–23. (in Russian)
2. Viksnin I.I., Komarov I.I., Maslennikov O.S., Muradov A.R., Pantiukhin I.S., Iureva R.A. Anomaly detection method for discovery of new cyber attacks on cyber physical systems. Automation in Industry, 2018, no. 2, pp. 58–62. (in Russian)
3. Peng Y., Lu T., Liu J., Gao Y., Guo X., Xie F. Cyber-physical system risk assessment. Proc. 9th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2013), Beijing, China, 2013, pp. 442–447. doi: 10.1109/IIH-MSP.2013.116
4. Semenov V.V., Lebedev I.S., Sukhoparov M.E., Salakhutdinova K.I. Application of an autonomous object behavior model to classify the cybersecurity state. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11660, pp. 104–112. doi: 10.1007/978-3-030-30859-9_9
5. Jones A., Kong Z., Belta C. Anomaly detection in cyber-physical systems: A formal methods approach. Proc. 53rd IEEE Annual Conference on Decision and Control (CDC 2014), 2014, pp. 848–853. doi: 10.1109/CDC.2014.7039487
6. Semenov V., Salakhutdinova K., Lebedev I., Sukhoparov M. Identification of abnormal functioning during the operation devices of cyber-physical systems. Journal of Applied Informatics, 2019, vol. 14, no. 6(84), pp. 114–122. (in Russian). doi: 10.24411/1993-8314-2019-10053
7. Narang P., Sikdar B. Anomaly detection in diurnal CPS monitoring data using a local density approach. Proc. 24th IEEE International Conference on Network Protocols, (ICNP 2016), 2016, pp. 7785323. doi: 10.1109/ICNP.2016.7785323
8. Meleshko A.V., Desnitsky V.A., Kotenko I.V. Machine learning based approach to detection of anomalous data from sensors in cyber-physical water supply systems. IOP Conference Series: Materials Science and Engineering, 2020, vol. 709, no. 3, pp. 033034. doi: 10.1088/1757-899X/709/3/033034


Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2024 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.

Яндекс.Метрика