The Case for Cyber Security in the Water Sector

Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine

This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.

Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap

Water supply systems are dynamic in nature, owing to the effect of climate change and consumer demand uncertainties. The operation of such a system must be managed effectively to meet up with the uncertainties, thus posing a key challenge. Unfortunately, previous information and communication technology (ICT) solutions could not provide the necessary support for applications to deal with the dynamics of the changing physical environment. Nevertheless, tremendous growth in technology offers better possibilities to enhance water supply systems’ operations. As a result, development in technology in sensing and instrumentation, communication and networking, computing and control is now jointly integrated with water supply system infrastructures to enhance the water system operations. One such technological paradigm shift is the cyber-physical system (CPS). In this paper, we present the concept of the CPS in the water system context and investigate the CPS applications to water supply system monitoring. Also, the various applications of CPSs and the application domain requirements are outlined. More importantly, research studies on its application to water system monitoring are scrutinized. As such, key challenges sounding the applications in WSSs are identified. We then outline the areas of improvement for further studies.

Perspectives on Harmful Algal Blooms (HABs) and the Cyberbiosecurity of Freshwater Systems

Harmful Algal Blooms (HABs) have been observed in all 50 states in the U.S., ranging from large freshwater lakes, such as the Great Lakes, to smaller inland lakes, rivers, and reservoirs, as well as marine coastal areas and estuaries. In 2014, a HAB on Lake Erie containing microcystin (a liver toxin) contaminated the municipal water supply in Toledo, Ohio, providing non-potable water to 400,000 people. Studying HABs is complicated as different cyanobacteria produce a range of toxins that impact human health, such as microcystins, saxitoxin, anatoxin-a, and cylindrospermopsin. HABs may be increasing in prevalence with rising temperatures and higher nutrient runoff. Consequently, new tools and technology are needed to rapidly detect, characterize, and respond to HABs that threaten our water security. A framework is needed to understand cyber threats to new and existing technologies that monitor and forecast our water quality. To properly detect, assess, and mitigate security threats on water infrastructure, it is necessary to envision water security from the perspective of a cyber-physical system (CPS). In doing so, we can evaluate risks and research needs for cyber-attacks on HAB-monitoring networks including data injection attacks, automated system hijacking attacks, node forgery attacks, and attacks on learning algorithms. Herein, we provide perspectives on the research needed to understand both the threats posed by HABs and the coupled cyber threats to water security in the context of HABs.

Model-based approach for cyber-physical attack detection in water distribution systems

Modern Water Distribution Systems (WDSs) are often controlled by Supervisory Control and Data Acquisition (SCADA) systems and Programmable Logic Controllers (PLCs) which manage their operation and maintain a reliable water supply. As such, and with the cyber layer becoming a central component of WDS operations, these systems are at a greater risk of being subjected to cyberattacks. This paper offers a model-based methodology based on a detailed hydraulic understanding of WDSs combined with an anomaly detection algorithm for the identification of complex cyberattacks that cannot be fully identified by hydraulically based rules alone. The results show that the proposed algorithm is capable of achieving the best-known performance when tested on the data published in the BATtle of the Attack Detection ALgorithms (BATADAL) competition (http://www.batadal.net).