Application of Artificial Neural Network for Cyber-Attack Detection in Water Distribution Systems as Cyber Physical Systems

2019 ◽  
pp. 82-88
Author(s):  
Kyoung Won Min ◽  
Young Hwan Choi ◽  
Abobakr Khalil Al-Shamiri ◽  
Joong Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Cyber Physical Systems ◽  
Attack Detection ◽  
Cyber Attack ◽  
Physical Systems ◽  
Artificial Neural

Detecting Cyber-Physical Attacks in Water Distribution Systems: One-class Classifier Approach

2020 ◽  
Author(s):  
Mashor Housh ◽  
Noy Kadosh ◽  
Alex Frid
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Attack Detection ◽  
Support Vector ◽  
Features Selection ◽  
Cyber Attack ◽  
Detection Problem ◽  
One Class Classification

<p>Water Distribution Systems (WDSs) are critical infrastructures that supply drinking water from water sources to end-users. Smart WDSs could be designed by integrating physical components (e.g. valve and pumps) with computation and networking devices. As such, in smart WDSs, pumps and valves are automatically controlled together with continuous monitoring of important systems' parameters. However, despite its advantage of improved efficacy, the automated control and operation through a cyber-layer can expose the system to cyber-physical attacks. One-Class classification technique is proposed to detect such attacks by analyzing collected sensors' readings from the system components. One-class classifiers have been found suitable for classifying "normal" and "abnormal" conditions with unbalanced datasets, which are expected in the cyber-attack detection problem. In the cyber-attack detection problem, typically, most of the data samples are under the "normal" state, and only small fraction of the samples can be suspected as under-attack (i.e. "abnormal" state). The results of this study demonstrate that one-class classification algorithms can be suitable for the cyber-attack detection problem and can compete with existing approaches. More specifically, this study examines the Support Vector Data Description (SVDD) method together with a tailored features selection methodology, which is based on the physical understanding of the WDS topology. The developed algorithm is examined on BATADAL datasets, which demonstrate a quasi-realistic case study and on a new case study of a large-scale WDS.</p>

Application of Artificial Neural Network Model Based on Improved PSO in Water Supply Systems

2011 ◽  
Vol 267 ◽  
pp. 609-613
Author(s):  
Hong Xiang Wang ◽  
Wen Xian Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Microscopic Model ◽  
Macroscopic Model ◽  
Control Element ◽  
Calibration Data ◽  
Artificial Neural ◽  
Improved Pso

Parameter calibration, data collection and simulation to control element were used to improve the accuracy of microscopic model. In order to overcome the shortage of macroscopic model, theoretical and empirical equation was adopted. The artificial neural network based on PSO method was introduced to improve simulation ability of water distribution system model from microscopic model and macroscopic model. There are two hidden layers with a maximum of 64 nodes per layer in the model. The Particle Swarm Optimization (PSO) algorithm is implemented to optimize the node numbers of the hidden layers in the model. The study indicates that the artificial neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

scholarly journals Cyber-Attack Detection in Water Distribution Systems Based on Blind Sources Separation Technique

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 795
Author(s):  
Bruno Brentan ◽  
Pedro Rezende ◽  
Daniel Barros ◽  
Gustavo Meirelles ◽  
Edevar Luvizotto ◽  
...  
Keyword(s):  
Remote Control ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Urban Systems ◽  
Cyber Attack ◽  
Statistical Control ◽  
Fast Independent Component Analysis

Service quality and efficiency of urban systems have been dramatically boosted by various high technologies for real-time monitoring and remote control, and have also gained privileged space in water distribution. Monitored hydraulic and quality parameters are crucial data for developing planning, operation and security analyses in water networks, which makes them increasingly reliable. However, devices for monitoring and remote control also increase the possibilities for failure and cyber-attacks in the systems, which can severely impair the system operation and, in extreme cases, collapse the service. This paper proposes an automatic two-step methodology for cyber-attack detection in water distribution systems. The first step is based on signal-processing theory, and applies a fast Independent Component Analysis (fastICA) algorithm to hydraulic time series (e.g., pressure, flow, and tank level), which separates them into independent components. These components are then processed by a statistical control algorithm for automatic detection of abrupt changes, from which attacks may be disclosed. The methodology is applied to the case study provided by the Battle of Attack Detection Algorithms (BATADAL) and the results are compared with seven other approaches, showing excellent results, which makes this methodology a reliable early-warning cyber-attack detection approach.

scholarly journals Development of a Cyberattack Detection Model for a Water Distribution System using Water Quality and Hydraulic Criteria

2020 ◽  
Vol 20 (5) ◽  
pp. 47-56
Author(s):  
Kyoung Won Min ◽  
Young Hwan Choi ◽  
Joong Hoon Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Detection Performance ◽  
Economic Losses ◽  
Water Quality Criteria ◽  
Quality Factors ◽  
Artificial Neural

In recent years, Cyber-Physical Systems (CPSs) have been applied to Water Distribution Systems (WDSs) to facilitate efficient operation and maintenance. Since data are transmitted through the network in such systems, a cyberattack can disrupt the operation of WDSs, for example, by causing water supply reduction, water pollution, and economic losses. In the past few years, cyberattack detection algorithms and various cyberattack scenarios have been proposed. These studies considered either hydraulic factors, such as pipe velocity, nodal pressure, or tank level, or water quality factors. However, an algorithm which considers only one factor cannot prevent the various problems that may arise, such as water quality issues, and the hydraulic and quality factors have a correlation. Therefore, in this study, a framework was developed by considering both hydraulic and water quality factors. The proposed approach was applied to an artificial neural network model. Performance indicators were used to examine the detection performance according to the parameters of the artificial neural network. By comparing the detection performance when only hydraulic factors were considered and the performance when both hydraulic and water quality factors were considered, the effectiveness of the algorithm that consider both hydraulic and water quality factors was demonstrated. A cyberattack detection algorithm that considers both hydraulic and water quality criteria can be applicable in more realistic scenarios and contribute to the establishment of safe infrastructure for the entire process of designing and operating WDSs with CPSs.

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