Critical infrastructures (CI) in the water, energy, communication, transportation, and other sectors, are increasingly exposed to multifaceted hazards, ranging from extreme weather events to cyber‐physical attacks, as well as unprecedented environmental, socioeconomic, institutional and technological changes, that challenge the traditional “fail‐safe” design paradigm. This review article traces the evolution of the concept of resilience from its ecological and material science origins to its modern policy mandate in the planning, design, and operation of critical infrastructure systems against uncertainty. Various interpretations of resilience are examined and contextualized along other related, but different policy terms, like risk and sustainability, with which resilience is often conflated. Inherent traits or designed properties of systems that contribute towards resilience are also explored. The discussion highlights key challenges in operationalising resilience through quantifiable metrics while acknowledging the steps already taken to incorporate the concept into standards and policy. It is further argued that adopting a “stress-testing” approach to resilience quantification is an intuitive and powerful way to generate evidence-based resilience assessments. The need for cultivating a more “uncertainty-aware” design and management culture in the CI sectors is also discussed, and the critical role resilience has to play in such a culture is presented, as a direct, tangible response to the deep uncertainties that are emerging in the global physical, social and geopolitical landscape within which CIs are tasked to operate.

Intelligent Transportation Systems (ITS) are revolutionising modern mobility by leveraging advancements in 5G technology, smart sensors, and sophisticated data analytics. These advancements facilitate the exchange and decision making of information in real time, improving safety and efficiency. However, the heterogeneous and loosely connected nature of the ITS components presents significant challenges in evaluating and managing trust within the ecosystem. Traditional approaches, such as blockchain-based consensus mechanisms, peer-to-peer voting systems, and static rule-based trust models, struggle to evaluate trust uniformly across diverse components and data types in real time, leaving the system vulnerable to various threats. Recent studies explored Machine Learning (ML) techniques to address trust management in ITS. These advanced approaches offer promising solutions for processing large volumes of heterogeneous data, identifying complex patterns, and adapting to dynamic environments. However, most existing ML-based solutions focus on assessing trust for particular components, such as vehicles and roadside units (RSUs), rather than addressing the collective trust of the entire ITS ecosystem. This paper proposes a novel ML-based dynamic trust management system termed MLT. It employs a feedforward neural network and the Levenberg–Marquardt Algorithm to dynamically assess the trustworthiness of ITS components. The system incorporates a dynamic time decay factor and continuously updates the trust scores, allowing effective identification and isolation of malicious actors. Through extensive simulations, MLT outperforms baseline models by up to 10% in precision and 9% in F-measure across various attack scenarios. These results highlight the superior performance of MLT in accuracy and robustness compared to existing trust management models.

Space-based assets are essential for critical societal functions across sectors like energy, transportation, communication, agriculture, and government. As these services become more integrated into daily life and reliance on cyber–physical systems grows, the interconnectivity and commercialization of space assets increases the attack surface and cybersecurity risks. Recent incidents affecting space infrastructure underscore the urgent need for robust cybersecurity measures. Legislators in the EU and other countries are addressing cyber risks to space and ground assets by developing minimum protection requirements. To support these measures, this paper evaluates whether existing security metrics in the literature cover all NIST functions, categories, and subcategories in the Cybersecurity Framework 2.0 (CSF 2.0). This framework provides a strong foundation for industry sectors and can serve as a baseline to ensure compliance with directives like NIS2. Our analysis reveals imbalances in academic discourse, with certain CSF 2.0 functions underrepresented. Then, we propose new metrics to address unaddressed NIST categories and adapt existing metrics to better suit the space domain. Considering practical challenges in implementing and monitoring these metrics, we propose a tool to facilitate their calculation and visualize security status. We also present a case study resembling real-world space infrastructure that demonstrates our tool’s applicability and the value of the designed metrics. Our research has managerial implications, supporting managers, CIOs, and CISOs in making informed decisions, helping companies understand their security levels, and complying with existing and forthcoming space sector regulations. We advocate for using security metrics to assess compliance with regulations like NIS2, CER, or upcoming space laws, demonstrating to policymakers that metrics can be integrated into policies to enhance their effectiveness.

In recent years, public-private partnership (PPP) has been advocated for as a medium to build critical infrastructure resilience. PPPs provides an added value achieved from greater co-operation among public-private sector entities. Performance measurement is critical to identify the difficulties and challenges that affects the PPP in executing their role in building critical infrastructure resilience. While the PPP concept is not novel, the precise performance measurement of a PPP in critical infrastructure resilience has not been explored yet. Key performance resilience indicators provide an assessment to measure the performance of a PPP set up to build critical infrastructure resilience. Since the area of performance assessment of PPP in critical infrastructure resilience has not been examined yet, this study used quantitative approach to develop a performance index to help quantify the performance of the PPP in critical infrastructure resilience using fuzzy synthetic evaluation method. The performance index developed consists of four major critical performance criteria groupings: these include, Hazard Detection, Continuity plans, Disruptive event detection, Functional Performance. The evaluation model can be used to objectively determine the performance of a PPP in building critical infrastructure resilience.