International Journal of Critical Infrastructure Protection最新文献

In the recent years, dust storms (DSs) pose a serious threat to critical infrastructure such as power distribution networks (PDNs). During DSs, the contamination of insulators, increases the possibility of damage to the PDNs insulation system and flashover induced power outage may occur. Power outages disrupt the performance of other urban infrastructures and, in addition to heavy financial losses, cause public dissatisfaction. Although this issue is of particular importance in areas with humid climate, a few studies have been reported on PDNs resilience improvement against DSs. This paper proposes a novel cost-based optimization model to make PDNs more resilient to DSs considering uncertainties. The proposed model is based on the two-stage stochastic mixed-integer programming (SMIP). In the first stage, decisions are made to equip repair crews (RCs) with insulator washing machines, hardening distribution lines with silicone-rubber insulators (SIs), and deploy backup distributed generators (DGs). Decisions in the second stage include network reconfiguration, RCs routing, DGs power dispatch, and load shedding as the critical options for PDN outage management during/after DSs. Case studies are evaluated in the IEEE 69-bus test system and a real 209-bus PDN in Khuzestan province, a coastal province in southwestern Iran. The simulation results at different budget levels have confirmed the efficiency of the proposed model for cost-optimal resilience enhancement planning of PDNs against DSs.

Due to the proliferation of network devices and the presence of sensitive information, healthcare systems have become prime targets for cyber attackers. Therefore, it is crucial to design an efficient and accurate intrusion detection system (IDS) specifically tailored for healthcare systems. In this regard, we conducted a comprehensive comparative study on network security intrusion detection in healthcare systems. In order to tackle the challenges arising from information redundancy and noise in feature selection, we developed the Maximum Information Coefficient (MIC) method to effectively analyse the nonlinear relationships among traffic features. This method was utilized in a comparative analysis involving ten models on three datasets. The experiments demonstrated that the detection models using MIC-based feature selection outperformed other feature selection approaches, especially when applied to the WUSTL-EHMS-2020 dataset, which includes patients' biometric features. The MIC-enhanced Extreme Gradient Boosting detection model achieved remarkable results, attaining an accuracy of 95.01%, precision of 94.94%, and recall of 95.01%. These findings underscore the efficacy of our comparative study in safeguarding healthcare systems against cyber attacks. Furthermore, our study highlights the importance of feature selection and the incorporation of patient biometric features in healthcare IDS. It is imperative for medical managers to consider these factors when making informed decisions regarding cyber security measures.

This work describes the PRAETORIAN system, an advanced framework for the protection of critical infrastructures from combined physical and cyber threats. It integrates components for detecting threats and generating alerts related with the physical and the cyber domains of Critical Infrastructures (CIs). These alerts are correlated by a hybrid situation awareness system that calculates the cascading effects of the attacks. Finally, a response coordination system manages notifications and assists decision making by providing mitigation actions and sharing information with the public and relevant authorities. The PRAETORIAN system was extensively validated by a large number of end users and was demonstrated in real-world environments in terms of providing support for the detection and mitigation of physical and/or cyber attacks in CIs.

Technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT) have converged in driving the next wave of digital revolution. Amalgamating the aforementioned advancements with critical infrastructure (CI) can significantly help society by offering a quality of life and boosting the nation’s economy and productivity. However, the lack of cybersecurity in CI gave rise to advanced threats and vulnerabilities that hindered the aforementioned societal benefits. In this vein, the paper provides an in-depth analysis of cyber threats and risks associated with different critical infrastructures, such as the financial, agriculture, energy, and healthcare sectors. Further, we thoroughly investigate the staggering benefits of AI and, based on it, present an exhaustive solution taxonomy to showcase the competency of AI mechanisms in confronting cyberattacks on CI. The taxonomy specifically addresses issues like data privacy, algorithmic bias, and human-AI collaboration for CI. Further, we proposed an AI-based secure data exchange framework for smart grid CI, where we attempt to secure the sensor’s data (i.e., power consumption, energy readings, and network data) from malicious adversaries. The proposed framework is evaluated using statistical measures, such as accuracy, training time, and receiver operating characteristic (ROC) curve, and anomaly detection. Further, the paper examines the research challenges that still adhere to the critical systems and require stringent AI-based mechanisms to tackle them.

Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.

The resilience of interdependent critical infrastructure systems (ICISs) is critical for the functioning of society and the economy. ICISs such as power grids and telecommunication networks are complex systems characterized by a wide range of interconnections, and disruptions to such systems can cause significant socioeconomic losses. This vital role requires the adaptation of new tools and technologies to improve the modeling of such complex systems and achieve the highest levels of resilience. One of the trending tools in many research fields to model complex systems is machine learning (ML). In this article, a systematic review of the literature on ML applications in ICISs resilience is conducted, considering the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), to address the lack of knowledge and scattered research articles on the topic. The main objective of this systematic review is to determine the state of the art of ML applications in the area of ICISs resilience engineering by exploring the current literature. The results found were summarized and some of the future opportunities for ML in ICISs resilience applications were outlined to encourage resilience engineering communities to adapt and use ML for various ICISs applications and to utilize its potential.

Distinct native attribute fingerprinting is considered as a means to ensure the longevity of WirelessHART communication devices used in industrial automation and control systems. The aim is for these devices to reach full life expectancy using a technical cradle-to-grave lifecycle protection strategy. The protection addressed here includes pre-deployment near-cradle counterfeit device detection using active fingerprinting and operational mid-life rogue device detection using passive fingerprinting. The counterfeit and rogue device detection rates are estimated for 56 five-class multiple discriminant analysis models. Detection demonstrations include using three non-modeled devices to complete a total of 3 × 5 × 56 = 840 individual rogue and counterfeit device identity (ID) verification demonstrations. The device ID verification process uses binary accept/reject decisions with false positive outcomes used to estimate rogue and counterfeit device detection rates. For device ID verification using active fingerprints, the demonstrated counterfeit detection rate approached 99 % using only 15-of-99 available features—an approximate 85 % dimensional-reduction. Device ID verification using passive fingerprints was more challenging and the rogue detection rate approached 94 % using 120-of-243 available features—an approximate 50 % dimensional-reduction.. Collectively, the dimensionally-reduced implementations support efficiency improvement objectives required for providing near-cradle counterfeit device and mid-life operational rogue device detection in critical industrial automation and control systems.

Health care facilities play a crucial role in the well-being of society. They are heavily dependent on other critical infrastructures including water supply for their daily operations and failure of water supply can have severe consequences. However, health care facilities are often underprepared for this scenario. The study investigates the emergency preparedness of German health care facilities to water supply failures, utilizing a socio-technical system approach. Data from interviews with experts and a survey among German hospitals were evaluated to determine the social and technical preparedness measures, in order to identify barriers and potential approaches for solutions. The findings show that there are still many gaps and needs for improvement. In particular, the social components of emergency preparedness are often given insufficient attention, with a lack of awareness about vulnerabilities to water failures being a shortcoming. The results were used to develop a framework linking social and technical components of critical infrastructure systems. The framework demonstrates that although the technical infrastructure components form the basis of preparedness, preparedness levels depend on the functioning of social infrastructure. The results underline the necessity of understanding the interlinkages between social and technical components, which can help identify potential vulnerabilities to infrastructure failures and risks. The study highlights the urgent need to comprehend health care facilities as socio-technical systems.

Nowadays, assets in space are vital for the provision of critical societal functions such as transportation, communication, production and supply of food, agriculture, etc. The increasing adoption of services provided by assets in space in our every day life, as well as the high dependence on cyberphysical systems, the increased interconnection and the commercialization of space increase the attack surface and poses significant cybersecurity risks to the space infrastructure; several cybersecurity incidents have already threatened assets in space. This work systematically reviews existing studies on the cybersecurity of the space infrastructure, analyzes the main results of each work, organizes and systematizes the current knowledge in the field, and proposes future research directions towards improving the cybersecurity posture of assets in space.