# Visualization Assisted Approach to Anomaly and Attack Detection in Water Treatment Systems
## 2 Related Works
Currently, a fairly large number of studies are being conducted on digital water metering and related directions, including issues of water consumption, long medium and short-term prediction of such consumption, resource planning, explanation of user behavior, characteristics of water consumption and detection of leaks by using machine learning and data analysis methods [6]. [PERSON] et al. proposed a mechanism for monitoring water quality, including checking for contamination and other characteristics of water [7]. At the same time, a particular requirement is established to achieve a high quality of detection in conditions of possible inaccuracies and the absence of some feature's values. Based on the application of water clustering methods, [PERSON] et al. built a mechanism for detecting pollution with certain values of the detection quality. In [8], the correlation between the properties of pollution of terrestrial natural water bodies and geographic characteristics of the location, such as latitude, longitude and height, is considered. In that work, a number of polluting factors (contaminating factors, e.g., water temperature, turbidity, acidity, the presence of oxygen in water, chlorides, etc.) are identified, and they are used to predict specific pollution based on physical parameters. The data analysis is performed using machine learning methods, namely four regression models. A peculiarity of this work is the manual collection and physical and chemical analysis of water to obtain a set of data on the pollution characteristics in various areas of the region under consideration, followed by the use of particular methods of data mining. Artificial intelligence methods are also used to determine the quality of water. In particular, [PERSON] et al. proposed a method for predicting the concentration of Escherichia coli bacteria in water bodies without involving complex specific laboratory procedures, which are time-consuming and technically complex and would require higher financial costs [9]. Specifically, [PERSON] et al. were able to build a machine learning model that, based on 10 simple physical, chemical and weather characteristics that can be easily measured, generates concentration predictions for a given bacterial species by using basic machine learning methods such as SVM, KNN, DT and others. In addition, the authors note the possibility of automating monitoring and elucidating water pollution using wireless sensor networks, such as LPWAN, which allow nodes to operate autonomously for a long time without replenishing energy resources. An anomaly detection process may significantly benefit from the application of the visualization techniques as they allow presenting data in clear and easily perceived form. Numerous research papers are devoted to the design of the visualization techniques applicable to the anomaly detection problem [10; 11]. For example, [PERSON] et al. [12] reviewed 150 papers and outlined four main application domains for visualization-driven detection of abnormal entity behavior: network communication, social interactions, financial transactions, and travel data. In [13], the authors studied different visualization techniques designed specifically for revealing anomalous activity in the network traffic. However, there are only a few works in the field of visualization-assisted anomaly detection in data from physical sensors. This fact could be explained by the fact that all technological processes are formalized, and any deviation from the predefined process could be detected on the basis of a set of established rules, and the exception is constituted by the problem of equipment failure forecasting, where different machine learning and visualization-driven approaches have been proposed [14]. Commercial SCADA systems [15; 16] and commercial solutions for anomaly detection in industrial IoT systems [17] mostly utilize standard visualization techniques such as timelines, gauges or mnemonic object diagrams for graphical representation and analysis of system parameters.