International Journal of Critical Infrastructure Protection最新文献

Traffic congestion is a global issue, which occurs during rush hour but also in situations of emergency causing massive congestion. This paper proposes a method for building a weighted road network by using the real-time traffic situation and the inherent characteristics of Urban Road Networks (URNs). To research the cascading failure vulnerability of URNs three kinds of node importance indexes are constructed from the structure, function, and traffic flow characteristics. Then the feasibility and validity of the proposed model are verified by taking Shanghai road networks (SRNs) as an example. The results indicate that the highest betweenness node-based attack causes the most damage to the SRNS of different types of attacks, and the SRNS cascade fails with the greatest speed and scale. Furthermore, we explore that the correlations between network vulnerability indicators, and suggest significant differences at different times during cascading failures of the weighted road network.

Modern commercially-available vehicles have active or passive keyless entry systems. In the case of an active keyless entry system, an individual presses a button on a key fob that transmits a radio frequency signal to its paired vehicle that unlocks the doors. In the case of a passive keyless entry system, a vehicle senses an approaching individual and signals the key fob to respond with a radio frequency signal. If the key fob is paired with the vehicle, then the vehicle unlocks the doors and may even start the engine. For user convenience, most modern vehicles have integrated active–passive keyless entry systems.

A passive keyless entry system in a vehicle uses the four-way handshake protocol to detect a proximal key fob and authenticate the key fob as being paired with the vehicle. Any vehicle or individual that transmits the initial protocol signal forces a key fob to become a passive radio signal transmitter. Exploiting the four-way handshake protocol has privacy and security consequences. This research demonstrates that passive keyless entry key fobs – and by extension, their paired vehicles and drivers – can be identified and tracked in real time using radio frequency signals. Additionally, this research demonstrates that the identification and tracking of key fobs, paired vehicles and drivers can be performed using commercial off-the-shelf hardware costing less than $900.

Network expansion of interdependent critical infrastructures under disruption uncertainty is modeled as a mixed-integer two-stage stochastic multi-objective optimization program. In this model, expected total cost and expected post-disaster resilience are considered competing objectives. Network resilience is quantified through network complexity and unmet demand. Functional relationships between critical infrastructures are modeled using a network-based approach with the physical interdependency enforced through demand constraints. Uncertainty is introduced as a set of random parameters corresponding to disruption scenarios. The proposed model is demonstrated in a case study of coupled power–water networks with the power flow in the grid modeled using linear DC power flow approximation equations. The deterministic-equivalent multi-objective model is solved using the augmented $ϵ$-constraint method. Solutions from stochastic and deterministic models are compared and the value of stochastic optimization is discussed.

A smart grid (SG) consists of an interconnection of an electrical grid, communication, and information networks. The rapid developments of SG technologies have resulted in complex cyber–physical systems. Due to these complexities, the attack surfaces of SGs broaden, and their vulnerabilities to cyber–physical threats increase. SG security systems focus on the protection of significant units and sub-systems of communication and power networks from malicious threats and external attacks. False data injection attack (FDIA) is known as the most severe threat to SG systems. In this paper, a method of optimizing convolutional neural networks — long short-term memory (CNN-LSTM) with particle swarm optimization (PSO) to detect FDIA in the SG system is proposed. This model uses phasor measurement unit (PMU) measurements to detect an abnormal measurement value and determine the type of this anomaly. The complex hyperparameter space of the CNN-LSTM is optimized by the PSO. A detailed numerical comparison is made using the state-of-the-art deep learning (DL) architectures like LSTM, PSO-LSTM, and CNN-LSTM models to verify the accuracy and effectiveness of the proposed model. The results show that the model outperforms other DL models. In addition, the model has a high accuracy rate that provides decision support for the stable and safe operation of SG systems. In this respect, the proposed detection model is a candidate for building a more robust and powerful detection and protection mechanism.

With the maturity of software-defined network (SDN) technology, its application in power communication networks (PCNs) is being introduced. SDN controllers can assign working and backup routes for arriving serve requests and provide one-to-one (1:1) protection, which is crucial for the transmission of power system data with high reliability and delay requirements. For PCNs in SDN architecture, a critical link identification algorithm based on link-related risk (LRR-CLIA), which considers both working and backup routes between nodes, is proposed in this paper. The algorithm calculates link importance to identify critical links by quantifying the impact of links on the network risk on service layer, transport layer, and topology layer. To verify the effectiveness of the LRR-CLIA, we compare the network loss on service layer, transport layer, topology layer, and comprehensive layer with other algorithms after ranking and removing the identified critical links in descending order. In the simulation results, the LRR-CLIA outperforms the other algorithms by an average of 39.5% and 51.77% in the small PCN and medium-scale PCN respectively, which shows that the LRR-CLIA can identify the critical links more effectively and accurately in PCNs whose services have both working and backup paths.

In recent years, some game models were proposed to protect critical infrastructure networks. But they mainly focused on the protection of key nodes, and there are rarely models to consider the fixed-point use of resources. Hence, in this paper, we propose a non-zero-sum simultaneous game model based on the Cournot model. Meanwhile, we presented a novel method of critical node centrality identification based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Simulating the game analysis on scale-free networks, small-world networks and random networks, it is found that the fixed operating nodes and the network topology are key factors in payoffs considering the constraints of resources. Besides, robustness analysis of networks on various sensitivity parameters is given and some effective optimal strategies are acquired to provide decision support for policy-makers.

This study explores the concept of public private partnership (PPP) in building critical infrastructure resilience (CIR) by looking at the attributes and objectives of the key stakeholders in PPP; government and private critical infrastructure (CI) operators. Although extant studies have been conducted on critical infrastructure resilience, the concept of PPP in CIR has not received much attention. This study conducted a systematic review on the objectives of the government and private CI operators in PPP to build CIR. A systematic methodology was used to retrieve 22 relevant publications that were subjected to content analysis to identify the objectives of PPP in CIR. 20 set of objectives were derived from the selected publications. In addition, social capital theory was used to explore the attributes of PPP in CIR. A conceptual framework was developed with the objectives and attributes of PPP in CIR. Some of the objectives included, conducting national risks assessment and vulnerability assessments, national critical infrastructure resilience plan and identifying what constitutes CI. In addition, social capital theory was used to explore the attributes of PPP in CIR. The findings outline the responsibilities of the government and private CI operators in partnership to build the resilience of critical infrastructure.

Ships and maritime infrastructure are becoming increasingly interconnected as the maritime industry is undergoing the industry 4.0 revolution. This development is associated with novel risk types such as the increased potential for successful cyberattacks. Several review studies have investigated the regulatory framework in connection to maritime cybersecurity, the vulnerabilities in maritime systems, potential cyberattack scenarios, and risk assessment techniques. None of them though, has implemented a systematic literature review and bibliometric analysis of the available academic research studies in the discipline of maritime cybersecurity. The aim of this review, therefore, is to offer a succinct description of the progress in academic research on the arising topic of maritime cybersecurity. To that end, we conducted a bibliometric analysis of maritime cybersecurity-related studies based on several metrics and analysis tools, identified the topics of academic research in this field, the employed methodologies and identified the main research challenges and directions in connection to maritime cybersecurity. To achieve the objectives, we employed principles from Preferred Reporting Items for Systematic reviews and Metanalysis (PRISMA) for systematic literature review and tailored keywords during a search in Scopus. The results demonstrated that Norway, the United Kingdom, France and the USA are the leading countries in maritime cybersecurity based on the weighted number of authors. The results also demonstrated that the main research focus in the area was on the development or application of cybersecurity risk assessment techniques and the design of monitoring and intrusion detection tools for cyberattacks in maritime systems. Based on the analysed literature, 53 challenges in various studies were identified and 73 topics for future research were suggested.

The smart grid has gained a reputation as the advanced paradigm of the power grid. It is a complicated cyber-physical system that combines information and communication technology (ICT) with a traditional grid that can remotely control operations. It provides the medium for exchanging real-time data between the company and users through the advanced metering infrastructure (AMI) and smart meters. However, smart grids have many security and privacy concerns, such as intruding sensitive data, firmware hijacking, and modifying data due to the high reliance on ICT. To protect the power-grid system from these counteracts and for reliable and efficient power distribution, early and accurate identification of these issues needs to be addressed. The intrusion detection in a smart grid system plays an essential role in providing a secure service and transmitting the high priority alert message to the system admin about the detection of adversary attacks. This paper proposes an intelligent intrusion detection scheme to accurately classify various attacks on smart power grid systems. The proposed scheme used the binary grey wolf optimization-based feature selection. It optimized the ensemble classification approach to learn the non-linear, overlapping, and complex electrical grid features taken from publicly available Mississippi State University and Oak Ridge National Laboratory (MSU-ORNL) dataset. The experimental results using a 10-fold cross-validation setup and selected feature subset for two class and three class problems reveal the proposed method's promising performance. Further, the significantly superior performance compared to the existing benchmark methods justified the robustness of the proposed scheme.