The water sector is facing emerging challenges, as cyber-physical threats target Supervisory Control and Data Acquisition (SCADA) systems of water utilities. A cyber-physical stress-testing platform is presented in this work, named RISKNOUGHT, which is able to model water distribution networks as cyber-physical systems, simulating the information flow of the cyber layer and the feedback interactions with the physical processes under control. RISKNOUGHT utilizes an EPANET-based solver for the physical process and a customizable network model for the SCADA system, capable of implementing complex control logic schemes within a simulation. The platform enables the development of composite cyber-physical attacks on various elements of the SCADA, including sensors, actuators, and PLCs, assessing the impact they have on the hydraulic response of the distribution network and the level of service. The platform is tested on a proof-of-concept benchmark network with promising results that demonstrate that the platform can form an innovative cyber-physical tool to support strategic planning and risk management.

The identification and assessment of the cyber-physical-threat landscape that surrounds water systems in the digital era is governed by complex socio-technical dynamics and uncertainties that exceed the boundaries of traditional risk assessment. This work provides a remedy for those challenges by incorporating socio-technical modelling to account for the adaptive balance between goal-driven behaviours and available skills of adversaries, exploitable vulnerabilities of assets and utility’s security posture, as well as an uncertainty-aware multi-scenario analysis to assess the risk level of any utility against cyber-physical threats. The proposed risk assessment framework, underpinned by a dedicated modelling chain, deploys a modular sequence of processes for (a) the estimation of vulnerability-induced probabilities and attack characteristics of the threat landscape under a spectrum of adversaries, (b) its formulation to a representative set of stochastically generated threat scenarios, (c) the combined cyber-physical stress-testing of the system against the generated scenarios and (d) the inference of the system’s risk level at system and asset level. The proposed framework is demonstrated by exploring different configurations of a synthetic utility case study that investigate the effects and efficiency that different cyber-security practices and design traits can have over the modification of the risk level of the utility at various dimensions.

While flood risk is evolving as one of the most imminent natural hazards and the shift from a reactive decision environment to a proactive one sets the basis of the latest thinking in flood management, the need to equip decision makers with necessary tools to think about and intelligently select options and strategies for flood management is becoming ever more pressing. Within this context, the Preparing for Extreme and Rare Events in Coastal Regions (PEARL) intelligent knowledge-base (PEARL KB) of resilience strategies is presented here as an environment that allows end-users to navigate from their observed problem to a selection of possible options and interventions worth considering within an intuitive visual web interface assisting advanced interactivity. Incorporation of real case studies within the PEARL KB enables the extraction of (evidence-based) lessons from all over the word, while the KB’s collection of methods and tools directly supports the optimal selection of suitable interventions. The Knowledge-Base also gives access to the PEARL KB Flood Resilience Index (FRI) tool, which is an online tool for resilience assessment at a city level available to authorities and citizens. We argue that the PEARL KB equips authorities with tangible and operational tools that can improve strategic and operational flood risk management by assessing and eventually increasing resilience, while building towards the strengthening of risk governance. The online tools that the PEARL KB gives access to were demonstrated and tested in the city of Rethymno, Greece.

This study presents an approach for the assessment of cyber-physical threats to water distribution networks under the prism of the uncertainty which stems from the variability and stochastic nature of nodal water demands. The proposed framework investigates a single threat scenario under a spectrum of synthetic, yet realistic, system states which are driven by an ensemble of stochastically generated nodal demands. This Monte Carlo-type experiment enables the probabilistic inference about model outputs, and hence the derivation of probabilistic estimates over consequences. The approach is showcased for a cyber-physical attack scenario against the monitoring and control system of a benchmark network.

Our modern urban environment relies on critical infrastructures that serve vital societal functions, such as water supply and sanitation, which are exposed to various threats of both physical and cyber nature. Despite the progress in protection and increased vigilance, long-established practices within the water utilities may rely on precarious methods for the characterization and assessment of threats, with uncertainty pertaining to risk-relevant data and information. Sources for uncertainty can be attributed to e.g. limited capabilities of deterministic approaches, siloed analysis of water systems, use of ambiguous measures to describe and prioritise risks or common security misconceptions. To tackle those challenges, this work brings together an ensemble of solutions, to form a novel, unified process of resilience assessment for the water sector against an emerging cyber-physical threat landscape e.g., cyber-attacks on the command and control sub-system. Specifically, the proposed framework sets out an operational workflow that combines, inter alia, a) an Agent-Based Modelling (ABM) approach to derive alternative routes to quantify risks considering the dynamics of socio-technical systems, b) an adaptable optimisation platform which integrates advanced multi-objective algorithms for system calibration, uncertainty propagation analysis and asset criticality prioritization and c) a dynamic risk reduction knowledge-base (RRKB) designed to facilitate the identification and selection of suitable risk reduction measures (RRM). This scheme is overarched by a cyber-physical testbed, able to realistically model the interactions between the information layer (sensors, PLCs, SCADA) and the water distribution network. The testbed is designed to assess the water system beyond normal operational capacity. It facilitates the exploration of emergent and unidentified threats and vulnerabilities leading to Low Probability, High Consequence (LPHC) events that systems are not originally designed to handle. It also evaluates alternative risk treatment options against case-appropriate indicators. The final product is the accretion of actionable information to integrate risk into decision-making in a practical and standardized form. Our work envisions to bring forth state-of-art technologies and approaches for the cyber-wise water sector. We aspire to enhance existing capabilities for large utilities and enable small and medium water utilities with typically less resources, to reinforce their systems’ resilience and be better prepared against cyber-physical and other threats.

Water systems are rapidly transforming into cyber-physical systems. Despite the benefits of remote control and monitoring, autonomous operation and connectivity, there is an expanded threat surface, which includes cyber-physical attacks. This study demonstrates a stress-testing methodology that focuses on assessing the performance of a contamination warning system, designed with alternative water quality (WQ) sensor placement strategies against cyber-physical attacks. The physical part of the attacks consists of backflow injection attacks with a contaminant, while the cyber part comprises cyber-attacks to the contamination warning system. The WQ sensor designs are generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT), based on optimizing various metrics. The coupled WDN and CPS operation, the deliberate contamination events, and the cyber-physical attacks, are simulated with the water system cyber-physical stress-testing platform RISKNOUGHT. Multidimensional resilience profile graphs are utilized to analyze performance, demonstrated in a benchmark case study. This type of assessment can be useful in risk assessment studies for water utilities as well as in WQ sensor placement optimization.