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.

The timely and robust detection of anomalies is essential for resilient and secure operations of critical water infrastructures against operational faults or malicious actions. However, real-world systems exhibit diverse and evolving spatiotemporal relationships among their components, posing an intricate challenge in anomaly detection. This study proposes a Hybrid Dynamic graph neural network that jointly maps long- and short-term spatiotemporal relationships in multivariate data streams. Those relationships are encoded via a hybrid graph, comprising an optimally learned static subgraph for persistent relationships and a complementary dynamic subgraph for dynamically shifting relationships. Additionally, an attention mechanism captures time-varying relational importances and shifts the model's focus towards significant relationships, while minimising contributions of less importance to the final outputs. The proposed architecture is showcased through a synthetic case study of a water distribution system with multivariate data streams from both on-site and soft sensors, and shows strong detection and localisation accuracy against all scenarios of operational faults and malicious actions explored. A comparative analysis with an equivalent static graph model indicates that the addition of the hybrid dynamic component enhances detection accuracy and reduces false alarm rates through more robust characterisation of behaviours, thus allowing actionable insights for more resilient and secure operations.

A self-consistent formulation for the force field and the associated dipole force tensor is presented. In the tight-binding approximation of the Hartree-Fock scheme, the variation of the total energy of the solids-up to first order in the atomic displacement-is obtained in terms of neutral quantities with respect to the frozen crystal. Improvements from previous estimations concerning dilute substitutional and interstitial alloys are reported. Also, the authors present-for the first time-a derivation of the force field arising from the presence of the surface of a transition metal.

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.