International Journal of Critical Infrastructure Protection最新文献

Critical Infrastructures (CI) underpin the basic functioning of society and the economy. Proper governance of CI security management remains a crucial challenge. This study aims to construct a knowledge graph for modeling CI protection. While the previous research has focused on threat intelligence modeling and open knowledge bases, they miss considering the defense side. Accordingly, we propose a knowledge graph for critical infrastructure protection, CIPKG, that extends the management ontology to include the defense side. It addresses the cross-industry and cross-time information gaps that occur in the process of CI protection management, making it more comprehensive in structure than the existing knowledge graph. We employ simplified Structured Threat Information Expression as attack ontology and design a new ontology for the defense side, which could combine with the existing threat ontology to form the CI protection knowledge graph. To dynamically extract information from emerging knowledge, we employ a Bi-directional Long Short-Term Memory and Conditional Random Field model with pre-trained cybersecurity domain-specific Bidirectional Encoder Representations from Transformers to recognize the named entities from CI regulations and standards. To associate the threat part with the management portion of the knowledge graph, we adopt the Knowledge Graph Bidirectional Encoder Representations from Transformer model to capture the semantic information and predict the relationship between threat and management. After information extraction and relation prediction, we build a knowledge graph with 529,360 nodes and about 3,335,000 edges.

This study explores network science algorithms for the robustness analysis of electricity networks. We first investigate the characteristics of key network models including random graphs, small-world, and scale-free networks. Then, various measures are explored for the robustness of such networks against failure or attack, utilizing topological features and percolation theory. Both weighted and unweighted scenarios are studied, with network voltage considered as the edge weight. For a case study, we investigate the network characteristics as well as the robustness of the Australian National Electricity Market (NEM) network on the basis of these models and theories.

The NEM is the world's longest interconnected power system, with an end-to-end distance of over 5000 km between the state of Queensland in the north and the state of South Australia. Our data contains 2375 transmission lines and 1538 nodes as generators or large demand customers. Our study shows that the NEM as an unweighted network is a small-world network (with exponential degree distribution). However, as a weighted network (considering the voltage capacity of nodes), it has a scale-free topology (following a power-law degree distribution). Robustness analysis revealed that the NEM presents relatively stronger robustness when facing random errors than when facing intentional attacks to nodes with a high degree centrality. It also revealed the sensitivity of the scale-free network to deliberate attacks directed toward important “hubs” (interconnected nodes).

Protecting critical infrastructure through visual surveillance is of vital importance, especially in underground entrance environments where large chunks of sloping glass ceilings are particularly susceptible to various types of terrorist activity. However, owing to the diversity of underground entrance environments, understanding them remains a challenge. Traditional 3D layout and object pose estimation that are evaluated on 3D point clouds or RGB-D data are energy-consuming and difficult to account for semantic information in environments. In this study, we present a methodology to understand underground entrance environments, and to recover their 3D reconstruction from a monocular camera. Clusters of sloping angle projections are extracted. Through their corresponding vanishing points (VPs), surfaces of sloping structures are estimated. Relative geometric constraints of different planes are built to bridge the gap between 2D sloping surfaces and 3D reconstruction without precise depth or point clouds. An underground entrance scene is approximated by Manhattan and sloping non-Manhattan structures in 3D reconstruction. The approach requires no prior training, and it requires neither the camera being calibrated nor the camera internal parameters being constant. Compared to the ground truth, the percentage of incorrectly understood pixels were measured and the results demonstrated that the method can successfully understand underground entrance scenes, meeting the requirements in safety monitoring for critical infrastructures from a resource-constrained surveillance camera.