Integrated approach to revelation of anomalies in wireless sensor networks for water control cases

  • Alexey Meleshko St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS, 198504, Saint Petersburg, Starii Petergof, Russia.
  • Anton Shulepov Saint Petersburg Electrotechnical University «LETI» 5, building 3, st. Professora Popova, 197376, Saint Petersburg, Russia
  • Vasily Desnitsky St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) St. Petersburg, Russia, 39, 14th Line V. O., 199178, Saint Petersburg, Russia.
  • Evgenia Novikova St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) St. Petersburg, Russia, 39, 14th Line V. O., 199178, Saint Petersburg, Russia.
Keywords: anomaly, detection, machine learning, visual analysis

Abstract

This article describes an approach to revelation of anomalies for Wireless Sensor Networks (WSN). It is based on the integration of visual data analysis techniques and data mining techniques. Feasibility of the approach has been confirmed on a demo case for WSN water management scenario. For verification we developed a software/hardware prototype of the network and a software model to generate the necessary data sets for the establishment of detection models and their investigation. The experiments carried out have shown a high quality of detection, which shows the applicability of the integrated approach to revelation of anomalies for use in practical cases.

Author Biographies

Alexey Meleshko, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS, 198504, Saint Petersburg, Starii Petergof, Russia.

Junior researcher laboratory of computer security problems of SPC RAS, meleshko.a@iias.spb.su

Anton Shulepov, Saint Petersburg Electrotechnical University «LETI» 5, building 3, st. Professora Popova, 197376, Saint Petersburg, Russia

Postgraduate of Information Systems Department of Saint Petersburg Electrotechnical University «LETI», aoshyleo@gmail.com

Vasily Desnitsky, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) St. Petersburg, Russia, 39, 14th Line V. O., 199178, Saint Petersburg, Russia.

PhD, Associate Professor, Senior Researcher Laboratory of Computer Security Problems of SPC RAS, desnitsky@comsec.spb.ru

Evgenia Novikova, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS) St. Petersburg, Russia, 39, 14th Line V. O., 199178, Saint Petersburg, Russia.

PhD, Associate Professor, Senior Researcher Laboratory of Computer Security Problems of SPC RAS, novikova@comsec.spb.ru

References

D. Levshun, D. Gaifulina, A. Chechulin, and I. Kotenko, “Problemnye voprosy informatsionnoi bezopasnosti kiberfizicheskikh sistem” [Problematic Issues of Information Security of Cyber- Physical Systems], Informatics and automation, vol. 19, no. 5, pp. 1050–1088, 2020 (in Russian); doi: 10.15622/ia.2020.19.5.6

J. Shin, Y. Baek, Y. Eun, and S. H. Son, “Intelligent sensor attack detection and identification for automotive cyber-physic systems,” in Proc. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA, 2017, pp. 1–8; doi: 10.1109/SSCI.2017.8280915

R. Wang, H. Song, Y. Jing, K. Yang, Y. Guan, and J. Sun, “A Sensor Attack Detection Method in Intelligent Vehicle with Multiple Sensors,” in Proc. 2019 IEEE International Conference on Industrial Internet (ICII), Orlando, FL, USA, 2019, pp. 219–226; doi: 10.1109/ICII.2019.00047

J. Inoue, Y. Yamagata, Y. Chen, C. M. Poskitt, and J. Sun, “Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning,” in Proc. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, USA, 2017, pp. 1058–1065; doi: 10.1109/ICDMW.2017.149

M. Raciti, J. Cucurull, and S. Nadjm-Tehrani, “Anomaly Detection in Water Management Systems,” J. Lopez, R. Setola, and S. D. Wolthusen, eds., Critical Infrastructure Protection, Heidelberg, Berlin: Springer, vol. 7130, pp. 98–119, 2012; doi: 10.1007/978-3-642-28920-0_6

E. Novikova, M. Bestuzhev, and I. Kotenko, “Anomaly Detection in the HVAC System Operation by a RadViz Based Visualization-Driven Approach,” S. Katsikas et al., eds., in Computer Security. CyberICPS 2019, SECPRE 2019, SPOSE 2019, ADIoT 2019, vol. 11980, 2020, pp. 402–418; doi: 10.1007/978-3-030- 42048-2_26

D. Herr, F. Beck, and T. Ertl, “Visual Analytics for Decomposing Temporal Event Series of Production Lines,” in Proc. 22nd International Conference Information Visualisation (IV), Fisciano, Italy, 2018, pp. 251–259; doi: 10.1109/iV.2018.00051

Y. Shi, Y. Liu, H. Tong, J. He, G. Yan and N. Cao, “Visual Analytics of Anomalous User Behaviors: A Survey,” IEEE Transactions on Big Data, vol. 14, no. 8, pp. 1–20, 2015; doi: 10.1109/TBDATA.2020.2964169

S. Y. Ji, B. K. Jeong, and D. H. Jeong, “Evaluating visualization approaches to detect abnormal activities in network traffic data,” Int. J. Inf. Secur., vol. 20, 331–345, 2020; doi: 10.1007/s10207-020-00504-9

A. Meleshko, V. Desnitsky, and I. Kotenko, “Machine learning based approach to detection of anomalous data from sensors in cyber-physical water supply systems,” IOP Conference Series: Materials Science and Engineering, vol. 709, pp. 1–7, 2019.

L. J. P. van der Maaten and G. E. Hinton, “Visualizing High-Dimensional Data Using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 11, pp. 2579–2605, 2008.

Published
2021-03-28
How to Cite
Meleshko, A., Shulepov, A., Desnitsky, V., & Novikova, E. (2021). Integrated approach to revelation of anomalies in wireless sensor networks for water control cases. Computer Tools in Education, (1), 58-67. https://doi.org/10.32603/2071-2340-2021-1-59-68
Section
Informational systems