Abstract
The distinctive features and key areas of analytical tools application for the operational monitoring and security analysis of cyber-physical systems of critical information infrastructure are highlighted. Problems of applying data analytics methods and technologies to ensure the security of cyber-physical systems at enterprises of the fuel and energy industries are described. The architectural solutions of the advanced security analytics platform are proposed. The chapter discusses the use of data analysis methods and technologies to ensure the security of cyber-physical systems at enterprises of the fuel and energy complex. Identified the distinctive features and highlighted the key areas of application of analytical tools of the system of operational monitoring and security analysis of cyber-physical systems of critical information infrastructure. Problem questions are formulated, the possibilities and limitations of advanced analytics tools in solving the tasks of ensuring the security of cyber-physical systems at the enterprises of the fuel and energy complex are defined. Architectural solutions of the advanced cybersecurity analytics platform are described. The presented results are based on the analysis of information from open sources: materials of scientific-practical conferences, analytical and technical reviews on the subject of security of industrial systems, and generalization of practical experience in the development, implementation, and support of integrated security systems at enterprises of the fuel and energy complex.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackerman, P.: Industrial Cybersecurity: Efficiently Secure Critical Infrastructure Systems. Packt Publishing Ltd., Birmingham (2017)
Anderson, K.: Analytical Culture: From the Data Collection to the Business Results. Mann, Ivanov and Ferber, Russia (1999)
Chernov, D., Sychugov, A.: Modern approaches to ensuring information security of automated process control systems. Tekhnicheskiye nauki 10, 58–64 (2018)
D’yakonov, A., Golovina, A.: Identification of anomalies in the mechanisms of machine learning methods. Analytics and data management in data intensive areas. In: Proceedings of XIX International Conference DAMDID/RCDL 2017 (pp. 469–476) (2017)
Filonov, P., Lavrentyev, A., Vorontsov, A.: Multivariate industrial time series with cyber-attack simulation: fault detection using an lstm-based predictive data model. arXiv preprint arXiv:1612.06676 (2016)
Idoine, C., Krensky, P., Linden, A., Brethenoux, E.: Magic quadrant for data science and machine-learning platforms (2019). https://www.gartner.com/en/documents/3899464/magic-quadrant-for-data-science-and-machine-learning-pla. Last accessed 29 April 2019
Islam, R.U., Hossain, M.S., Andersson, K.: A novel anomaly detection algorithm for sensor data under uncertainty. Soft. Comput. 22(5), 1623–1639 (2018)
Kavanagh, K., Sadowski, G., Bussa, T.: Magic quadrant for security information and event management (2018). https://www.gartner.com/en/documents/3894573/magic-quadrant-for-security-information-and-event-manage. Last accessed 29 April 2019
Kotenko, I., Levshun, D., Chechulin, A., Ushakov, I., Krasov, A.: An integrated approach to ensuring the security of cyber-physical systems based on microcontrollers. Voprosy kiberbezopasnosti 3(27), 29–38 (2018)
Kraevski, J., Ivanov, A.: Situational perception. A new approach to the design of human machine interfaces. Avtomatizatsiya v promyshlennosti 12, 26–30 (2014)
Lukashin, A., Lukashin, A.: Resource scheduler based on multi-agent model and intelligent control system for openstack. In: International Conference on Next Generation Wired/Wireless Networking (pp. 556–566). Springer, Berlin (2014)
Mart´ı, L., Sanchez-Pi, N., Molina, J., Garcia, A.: Anomaly detection based on sensor data in petroleum industry applications. Sensors 15(2), (2015)
Matveev, A.: Behavioral analysis systems market review —user and entity behavioral analytics (UBA/UEBA). https://www.anti-malware.ru/analytics/Market_Analysis/user-and-entity-behavioral-analytics-ubaueba. Last accessed 29 April 2019
McLaughlin, S., Konstantinou, C., Wang, X., Davi, L., Sadeghi, A.R., Maniatakos, M., Karri, R.: The cybersecurity landscape in industrial control systems. Proc. IEEE 104(5), 1039–1057 (2016)
Mehrotra, K.G., Mohan, C.K., Huang, H.: Anomaly Detection Principles and Algorithms. Springer, Berlin (2017)
Rieger, C., Manic, M.: On critical infrastructures, their security and resilience—trends and vision. arXiv preprint arXiv:1812.02710 (2018)
Sadowski, G., Bussa, T., Litan, A., Phillips, T.: Market guide for user and entity behavior analytics (2018). https://www.gartner.com/en/documents/3872885/market-guide-for-user-and-entity-behavior-analytics. Last accessed 29 April 2019
Sallam, R., Richardson, J., Howson, C., Kronz, A.: Magic quadrant for analytics and business intelligence platforms (2019). https://www.gartner.com/en/documents/3900992/magic-quadrant-for-analytics-and-business-intelligence-p. Last accessed 29 April 2019
Utkin, L.V.: A framework for imprecise robust one-class classification models. Int. J. Mach. Learn. Cybernet. 5(3), 379–393 (2014)
Utkin, L., Zhuk, J.: Robust anomaly detection model using clogging model. Vestnik komp’juternyh i informacionnyh tehnologij 7, 47–51 (2013)
Zegzhda, D., Vasiliev, Y., Poltavtseva, M., Kefeli, I., Borovkov, A.: Cybersecurity advanced production technologies in the era of digital transformation. Voprosy kiberbezopasnosti 2(26), 2–15 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nashivochnikov, N.V., Bolshakov, A.A., Lukashin, A.A., Popov, M. (2020). The System for Operational Monitoring and Analytics of Industry Cyber-Physical Systems Security in Fuel and Energy Domains Based on Anomaly Detection and Prediction Methods. In: Kravets, A., Bolshakov, A., Shcherbakov, M. (eds) Cyber-Physical Systems: Industry 4.0 Challenges. Studies in Systems, Decision and Control, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-030-32648-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-32648-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32647-0
Online ISBN: 978-3-030-32648-7
eBook Packages: EngineeringEngineering (R0)