Control and decision making of water supply systems influence economic, political, and social variables on several time horizons. Particularly in Central Asia, the international nature of water resources, large irrigation requirements, and depletion of surface water and aquifers in the arid region call for a systems-level framework to analyze disruptive scenarios and determine a schedule of risk countermeasures. This paper evaluates risk as the influence of scenarios on system order. Through scenario-based multi-criteria decision analysis, system initiatives are prioritized in the baseline and disruptive scenarios. The methods are applied to a case of water policies in Turkmenistan where twelve water policies are ordered according to six system criteria including social, situational, and economic factors. The system order is updated across each of seven disruptive scenarios relating to political, economic, technological, and societal trends and forecasts.

Disruption of complex infrastructures systems involves cascading failures and interdependencies. This paper presents a network-based approach to assessing infrastructure resilience using scenario-based disruptions that remove entire sectors from the network. This approach evaluates system-wide vulnerabilities by modeling structural failures through the removal of nodes from the infrastructure graph. The framework uses a directed graph to represent interdependencies and uses eigenvector centrality to rank sector influence. Disruptive scenarios, including power outages, communication failures, and hybrid threats are applied to evaluate changes in system order. Spearman’s rank correlation quantifies the disruptiveness of each scenario, identifying which sectors experience the most significant shifts in importance. Results show that disruptions to the communications sector cause the greatest reordering of system orders, while disruptions to water & wastewater have a lower impact. The analysis demonstrates how different hazards affect regional resilience and provides insights for decision-makers to schedule the risk countermeasures.

Decision making for resilient infrastructure systems requires methods for assessing risk across political, social, economic, and physical domains. Traditional risk assessments for physical systems often consider only historical data in prioritization and decision making. This paper presents a systems and risk analysis framework to identify resource threats to energy system development priorities. The methods are demonstrated for the case of alternative energy investments in Turkmenistan. Twenty-seven sub-basins are ordered according to three satellite-observed metrics representing energy generation potential. Disruptions to the baseline system order, induced by 30-year forward-looking projection scenarios, are used to identify the most and least resilient sub-basins in the context of energy system development.

The security of electronics and embedded hardware assets is critical to the operations of industrial organizations and facilities. Sources of risk, such as counterfeit parts, can compromise operations and result in degraded functionality and other negative impacts. However, accounting for and prioritizing numerous electronic assets within a company is difficult. This paper describes a security audit process that facilitates tracking and management of risks through a scenario-based methodology. The process allows facility managers to account for impacts to organizational security objectives such as confidentiality, integrity, availability, and accountability. The scenarios that are the most disruptive to system order can be determined. The results guide the implementation of risk reduction countermeasures. The paper is widely relevant to the identification and tracking of security vulnerabilities for a variety of large-scale systems.

Iraq's infrastructure's water and environmental security are influenced by its semi-arid to arid climate, marked by erratic variations, including minimal precipitation, increasing air temperatures, and compromised water quality resulting from reduced inflow in the tributary. The transportation sector plays a vital role in improving economic conditions and mitigating the impacts of climate change. However, critical transportation systems, essential for the movement of goods, information, and people, are jeopardized by water scarcity and climate change. This study develops a sensitivity analysis of the priorities among transportation nodes subject to their disruption by water scarcity and other emergent and future stressors. The stressors include social, technological, regulatory, workforce, market, climate, and hydrologic criteria. This analysis describes the nodes of highest importance and quantifies which scenarios are the most and least disruptive to the system order of nodes. This study includes thirteen order criteria, forty-three nodes, and seven risk scenarios. The paper should interest the system owners and operators who are concerned with monitoring their enterprises' resilience and environmental security.

The resilience of vulnerable populations to environmental extremes is a concern for policymaking across environmental justice, economic development, technology innovation, etc. This study models the resilience of system order for a portfolio of investments, focusing on the spatial distributions of environmental stressors (i.e., precipitation, temperature, soil moisture, and elevation), social vulnerability, and risk exposure. The methodology quantifies risk as a disruption of baseline order under each of several scenarios that combine social and environmental factors, with attention to vulnerable populations. A realistic example is described with features of a southeastern region of the USA. The results and methodology are a rationale for the allocation of investments for economic development and system resilience, balancing among several criteria of social vulnerability and environmental justice.

The security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system’s effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks’ security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15% accuracy and a 98.83% F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.

Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network-based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.

This paper develops a methodology for eliciting shifts in preference across future scenarios in the performance assessment of infrastructure policies and investments. The methodology quantifies the robustness of alternative portfolios across a variety of scenarios and identifies the scenarios that greatly affect the assessments. An innovation of the methodology is to elicit, for each scenario, only a few relative increases or decreases in importance of selected terms of the value function, which is more efficient than a full elicitation of the value function for each scenario. The identification of critical scenarios via our methodology can be used to focus resource-intensive and potentially costly modeling activities. The methodology integrates preference orders, centroid weights, and the Borda method. In a demonstration, the methodology assesses the relative sea level and other climate-change scenarios that could affect the performance of coastal protections.

Recent severe storm experiences in the U.S. Gulf Coast illustrate the importance of an integrated approach to natural disaster preparedness planning, one that harmonizes stakeholder and implementing agency efforts. Risk management decisions that are informed by and address decision maker and stakeholder risk perceptions and behavior are essential for effective risk management policy. Formal (versus ad hoc) analyses of risk manager and stakeholder cognition represent an important first step. Mental modeling has been successfully used to reveal, characterize and map stakeholder beliefs about risks in order to develop more effective cross-stakeholder communication strategies. This paper summarizes diagram-based representation of mental models, and presents an example specific to U.S. Army Corps of Engineers (USACE) flood preparedness and response program needs. Understanding flood risk mental models will enable USACE to bridge differences across and within stakeholder groups, cultures and disciplines internally and externally involved in natural disaster response in order to develop approaches for handling floods and other emerging challenges.