Abstract
In view of the close integration of robotic systems into industrial and technological systems, critical infrastructure objects, as well as a significant number of possible entry points, the task of monitoring operational safety for robotic systems is more complex than ensuring information security in classical information systems. The paper presents a method for monitoring the state of robotic systems based on time series analysis. The developed method differs from the existing ones by the combined approach of using an ensemble of parallel classifiers and Fishburn weight coefficients in the security event management system. The time series is composed of a set of informative features, characterizing the functioning of a robotic system. Values for previous discrete time points are ranked using significance weights. The method was approved on a data set of a real industrial system. Due to parallel computing, it was possible to significantly increase the speed of determining the state of robotic systems. The identification precision due to the combined approach increased by 1.45% compared to the best results presented in scientific papers, the recall increased by 4.45% and amounted to 99.85% for both indicators. The results of the study can be applied in monitoring the safety of robotic systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shukalov, A.V., Zakoldaev, D.A., Zharinov, I.O., Zharinov, O.O.: Control, computing and communication in industrial cyberphysical systems with feedback. J. Phys: Conf. Ser. 2094(4), 042036 (2021). https://doi.org/10.1088/1742-6596/2094/4/042036
Kotenko, I.V., Kribel, A.M., Lauta, O.S., Saenko, I.B.: Analysis of the process of selfsimilarity of network traffic as an approach to detecting cyber-attacks on computer networks. Electrosvyaz 12, 54–59 (2020). https://doi.org/10.34832/ELSV.2020.13.12.008
Vasilyev, V.I., Vulfin, A.M., Gvozdev, V.E., Kartak, V.M. Atarskaya, E.A.: Ensuring information security of cyber-physical objects based on predicting and detecting anomalies in their state. Syst. Control Commun. Secur. 6, 90–119 (2021). https://doi.org/10.24412/2410-9916-2021-6-90-119
Zegzhda, D.P., Pavlenko, E.Y.: Homeostatic security of cyber-physical systems. Inf. Secur. Prob. Comput. Syst. 3, 9–23 (2017)
Zaitceva, E.A., Zegzhda, D.P., Poltavtseva, M.A.: Applying of graph representation and case-based reasoning for security evaluation of computer systems. Inf. Secur. Prob. Comput. Syst. 2, 136–148 (2019)
Lavrova, D.S.: An approach to developing the SIEM system for the Internet of Things. Autom. Control. Comput. Sci. 50(8), 673–681 (2016). https://doi.org/10.3103/S0146411616080125
Vasiliev, Y.S., Zegzhda, P.D., Zegzhda, D.P.: Providing security for automated process control systems at hydropower engineering facilities. Therm. Eng. 63(13), 948–956 (2016). https://doi.org/10.1134/S0040601516130073
Sukhoparov, M.E., Lebedev, I.S., Semenov, V.V.: Information security state analysis of elements of industry 4.0 devices in information systems. LNCS 12525, 119–125 (2020). https://doi.org/10.1007/978-3-030-65726-0_11
Semenov, V.V.: An approach to the identification of the state of elements in cyber-physical systems based on principal component analysis. Sci. Tech. J. Inf. Technol. Mech. Optics 21(6), 887–894 (2021). https://doi.org/10.17586/2226-1494-2021-21-6-887-894
Kruegel, C., Toth, T.: Using decision trees to improve signature-based intrusion detection. LNCS 2820, 173–191 (2003). https://doi.org/10.1007/978-3-540-45248-5_10
Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. LNCS 10529, 45–68 (2017). https://doi.org/10.1007/978-3-319-66787-4_3
Goh, J., Adepu, S., Junejo, K.N., Mathur, A.: A dataset to support research in the design of secure water treatment systems. LNCS 10242, 88–99 (2017). https://doi.org/10.1007/978-3-319-71368-7_8
Kravchik, M., Shabtai, A.: Detecting cyber-attacks in industrial control systems using convolutional neural networks. In: Proceedings of the 47th Workshop on Cyber-Physical Systems Security and Privacy, pp. 72–83 (2018).https://doi.org/10.1145/3264888.3264896
Shalyga, D., Filonov, P., Lavrentyev, A.: Anomaly detection for water treatment system based on neural network with automatic architecture optimization. arXiv: 1807.07282 (2018). https://doi.org/10.48550/arXiv.1807.07282
Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C.M., Sun, J.: Anomaly detection for a water treatment system using unsupervised machine learning. In: Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1058–1065 (2017).https://doi.org/10.1109/ICDMW.2017.149
Kravchik, M., Shabtai, A.: Efficient cyber-attack detection in industrial control systems using lightweight neural networks and PCA. IEEE Trans. Dependable Secure Comput. 19(4), 2179–2197 (2022). https://doi.org/10.1109/TDSC.2021.3050101
Elnour, M., Meskin, N., Khan, K., Jain, R.: A dual-isolation-forests-based attack detection framework for industrial control systems. IEEE Access 8, 36639–36651 (2020). https://doi.org/10.1109/ACCESS.2020.2975066
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. LNCS 11730, 703–716 (2019). https://doi.org/10.1007/978-3-030-30490-4_56
Gómez, A., Maimó, L., Celdrán, A., Clemente, F.: MADICS: a methodology for anomaly detection in industrial control systems. Symmetry 12(10), 1583 (2020). https://doi.org/10.3390/sym12101583
Gaifulina, D.A., Kotenko, I.V.: Analysis of deep learning models for network anomaly detection in Internet of Things. Inf.-Upravliaiushchie Sist. 1, 28–37 (2021). https://doi.org/10.31799/1684-8853-2021-1-28-37
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Semenov, V. (2023). Monitoring the State of Robotic Systems Based on Time Series Analysis. In: Ronzhin, A., Sadigov, A., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2023. Lecture Notes in Computer Science(), vol 14214. Springer, Cham. https://doi.org/10.1007/978-3-031-43111-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-43111-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43110-4
Online ISBN: 978-3-031-43111-1
eBook Packages: Computer ScienceComputer Science (R0)