Ensuring the integrity of telemetric information on the state of a complex technical object

The relevance of the study is due to the need to improve the security of telemetry information transmitted to the manufacturer, which allows analyzing the state of on-board subsystems, failures and malfunctions, as well as detecting possible intruder impacts. On the basis of algorithms for predictive analytics of telemetric information, malfunctions and pre-failure states of on-board subsystems, and automatic control systems for the gas turbine engine of an aircraft can be detected. Efficient collection and analysis of telemetry helps specialists of ground technical services to plan repair and preventive measures. Telemetry accumulated over a long period makes it possible to continuously adapt digital models of aircraft subsystems, blocks and modules, which can be used to provide significant decision support in the event of a technical failure. In this regard, the purpose of the research is to increase the probability of detecting unauthorized modification of the transmitted telemetry data on the state of a complex technical object – a gas turbine engine automatic control system. The main method for studying this problem is the intellectual analysis of multidimensional time series of parameters characterizing the state of the onboard systems of the aircraft. The article develops the structure of the system for monitoring the integrity of telemetric information transmitted from the aircraft based on the analysis of the consistency of multidimensional time series. A comparison is made between telemetry from the aircraft and the outputs of a digital model of a complex technical product. At the output of the system, the probability estimate of integrity violation of the data transmitted to the manufacturer is formed. The materials of the article are of practical value for increasing the level of information protection when it is being transmitted from the aircraft to the manufacturer.

1. Vasilyev V.I., Zhernakov S.V. Classification of gas turbine engine operating modes using neural network technology. Vestnik UGATU. 2009;12(1):53–60. (In Russ.).

2. Vasilyev V.I., Zhernakov S.V., Musluhov I.I. On-board algorithms for control of gas turbine engine parameters based on neural networks technology. Vestnik UGATU. 2009;12(1):61–74. (In Russ.).

3. Boev N.M., Sharshavin P.V., Nigruca I.V. Building communication systems for unmanned aerial vehicles for transmitting information over long distances. Izvestiya JuFU. Tehnicheskie nauki. 2014;3(152):147–158. (In Russ.).

4. Gurevich O.S. et al. Wireless demo GTE control system. Sistemy avtomaticheskogo upravlenija aviacionnymi gazoturbinnymi dvigateljami: Trudy CIAM №1346. 2010:46–58. (In Russ.).

5. Bhoopathi Rapolu Internet of aircraft things: an industry set to be transformed. Available by: https://aviationweek.com/aerospace/connected-aerospace/internet-aircraft-things-industry-set-be-transformed (accessed on 10.12.2022).

6. Hugo Teso Aircraft Hacking. Practical Aero Series. n.runs Professionals. 2013. Available by: http://conference.hitb.org/hitbsecconf2013ams/materials/ (accessed on 10.12.2022).

7. Bandyopadhyay D., Sen J. Internet of things: Applications and challenges in technology and standardization. Wireless personal communications. 2011;58(1):49–69.

8. Niggemann O. et al. Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control. DX. 2015:185–192.

9. Gao W. et al. On SCADA control system command and response injection and intrusion detection. 2010 eCrime Researchers Summit. IEEE. 2010:1–9.

10. Zhang Y. et al. Distributed intrusion detection system in a multi-layer network architecture of smart grids. IEEE Transactions on Smart Grid. 2011;2(4):796–808.

11. Bigham J., Gamez D., Lu N. Safeguarding SCADA systems with anomaly detection. International workshop on mathematical methods, models, and architectures for computer network security. Springer, Berlin, Heidelberg. 2003:171–182.

12. He Q., Blum R.S. Smart grid monitoring for intrusion and fault detection with new locally optimum testing procedures. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011:3852–3855.

13. Kim M. et al. Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner. International journal of refrigeration. 2008;31(5):790–799.

14. Guzairov M.B. et al. The concept of integrity of telemetric information about the state of an aircraft power plant monitoring. 2019 International Conference on Electrotechnical Complexes and Systems (ICOECS). IEEE. 2019:1–6.

15. Frid A.I., Vulfin A.M., Berkholts V.V. The method of aviation gas turbine engine state information integrity monitoring. Bezopasnost Informatsionnykh Tekhnologiy. 2020;27(4):65–76. (In Russ.).

16. Frid A.I., Vulfin A.M., Berkholts V.V. Patent № 2740544 C1 Russian Federation, MPK G06F 21/31. Method and system for monitoring data integrity: № 2020122967: publ. 15.01.2021 (In Russ.).

17. Frid A.I. et al. Architecture of the security access system for information on the state of automatic control systems of aircraft. Acta Polytechnica Hungarica. 2020;17(8):151–164.

18. Frid A.I., Vulfin A.M., Berkholts V.V. Analysis of the methods of constructing information attack models for the system of telemetric information transmission. Proceedings of the 6th All-Russian Scientific Conference Information Technologies for Intelligent Decision Making Support (ITIDS'2018). 2018:226–229.

19. Frid A.I. et al. The architecture of the web application for protected access to the informational system of processing critically important information. Computer Science and Information Technologies (CSIT'2017). 2017:16–22.

20. Guzairov M.B. et al. Simulation modelling of the transmission system of the telemetric information on the status of the on-board aircraft status. Data Science. IV International Conference on Information Technology and Nanotechnology. 2018:105–111.

21. Berkholts V.V. et al. The structure of secure system for collection, storage and processing of telemetric information on the state of aircraft subsystems. Industrial 4.0. 2018;3(4):209–212.

22. Guzairov M.B. et al. Secure access to the database on the state of automatic control systems (ACS) of aircraft gas turbine engines through a web application. Informacija i bezopasnost. 2017;20(3):410–413. (In Russ.).

23. Gvishiani A.D. Fuzzy Comparisons and Anomaly Recognition in Time Series. Kibernetika i sistemnyj analiz. 2008;44(3):3–18. (In Russ.).

24. Jarushkina N.G., Afanas'eva T.V., Perfileva I.G. Time Series Mining: a Tutorial. Uljanovsk: UlGTU; 2010. 320 p. (In Russ.).

25. Armstrong J.S., Collopy F. Error measures for generalizing about forecasting methods: Empirical comparisons. International journal of forecasting. 1992;8(1):69–80.

26. Zagorujko N.G. Applied methods of data and knowledge analysis. Novosibirsk: IM SO RAN; 1999. 270 p. (In Russ.).