Next Article in Journal
Wildfire-Induced Changes in Flood Risk in Recreational Canyoning Areas: Lessons from the 2017 Jerte Canyons Disaster
Previous Article in Journal
Conceptual Framework for Modeling Dynamic Complexities in Produced Water Management
 
 
Article

Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems

1
St. Petersburg Federal Research Center of the Russian Academy of Sciences, 14-th Liniya, 39, 199178 St. Petersburg, Russia
2
Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Ul. Professora Popova 5, 197376 St. Petersburg, Russia
*
Authors to whom correspondence should be addressed.
Academic Editor: Marco Franchini
Water 2022, 14(15), 2342; https://doi.org/10.3390/w14152342
Received: 31 May 2022 / Revised: 9 July 2022 / Accepted: 26 July 2022 / Published: 29 July 2022
The specificity of the water treatment field, associated with water transmission, distribution and accounting, as well as the need to use automation and intelligent tools for various information solutions and security tools, have resulted in the development of integrated approaches and practical solutions regarding various aspects of the functioning of such systems. The research problem lies in the insecurity of water treatment systems and their susceptibility to malicious influences from the side of potential intruders trying to compromise the functioning. To obtain initial data needed for assessing the states of a water treatment system, the authors have developed a case study presenting a combination of a physical model and a software simulator. The methodology proposed in the article includes combining methods of machine learning and visual data analysis to improve the detection of attacks and anomalies in water treatment systems. The selection of the methods and tuning of their modes and parameters made it possible to build a mechanism for efficient detection of attacks in data from sensors with accuracy values above 0.95 for each class of attack and mixed data. In addition, Change_Measure metric parameters were selected to ensure the detection of attacks and anomalies by using visual data analysis. The combined method allows identifying points when the functioning of the system changes, which could be used as a trigger to start resource-intensive procedures of manual and/or machine-assisted checking of the system state on the basis of the available machine learning models that involve processing big data arrays. View Full-Text
Keywords: anomaly detection; machine learning; water treatment; visual analytics anomaly detection; machine learning; water treatment; visual analytics
Show Figures

Figure 1

MDPI and ACS Style

Meleshko, A.; Shulepov, A.; Desnitsky, V.; Novikova, E.; Kotenko, I. Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems. Water 2022, 14, 2342. https://doi.org/10.3390/w14152342

AMA Style

Meleshko A, Shulepov A, Desnitsky V, Novikova E, Kotenko I. Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems. Water. 2022; 14(15):2342. https://doi.org/10.3390/w14152342

Chicago/Turabian Style

Meleshko, Alexey, Anton Shulepov, Vasily Desnitsky, Evgenia Novikova, and Igor Kotenko. 2022. "Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems" Water 14, no. 15: 2342. https://doi.org/10.3390/w14152342

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop