Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber-Physical Systems in Smart Grids

Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber-physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids

Worm Epidemics in Wireless Adhoc Networks

A dramatic increase in the number of computing devices with wireless communication capability has resulted in the emergence of a new class of computer worms which specifically target such devices. The most striking feature of these worms is that they do not require Internet connectivity for their propagation but can spread directly from device to device using a short-range radio communication technology, such as WiFi or Bluetooth. In this paper, we develop a new model for epidemic spreading of these worms and investigate their spreading in wireless ad hoc networks via extensive Monte Carlo simulations. Our studies show that the threshold behaviour and dynamics of worm epidemics in these networks are greatly affected by a combination of spatial and temporal correlations which characterize these networks, and are significantly different from the previously studied epidemics in the Internet

Measurements and Evolution of Complex Networks with Propagation Dynamics

University of Technology Sydney. Faculty of Engineering and Information Technology.With the development of technology, we live in a world which is surrounded by complex networks, e.g., the power grid, transportation network, Internet, neural networks, social networks. Understanding the structure and dynamics of these extremely complex interactive networks has become one of the key research topics and challenges of life science in the 21st century. For example, the coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, hygienic habits and the communication methods. In order to control the virus spread, it is very necessary to analyze the network structures and epidemic dynamics, e.g., the importance of nodes in the networks, the influence of network structure measurements on propagation, the interaction between propagation dynamics and the structure measurements, and the construction of epidemiological models that can capture the effects of these changes in mobility on the spread of virus. Meanwhile, the results of these studies can also be used as a reference for the study of multiple propagation behaviors in other networks. Complex network theory is to study the commonness of these seemingly different complex networks and the universal methods to deal with them. In 1998 and 1999, the finding of small world effects and scale-free property has attracted a great deal of attention of network structures and dynamics, which raises the science awareness for the real world. After the discovery of small world effects and scale-free property of networks, researchers gradually realize and study the complexity of networks. More network structure metrics are proposed, and more network characteristics are found with the development of complex network research. […] In the thesis, the influence of complex network structure measurements on the propagation processes and the dynamic relationship between network structures and the propagation processes are studied. Firstly, the influence of network structure measurement on the propagation process is studied and applied to the process of node influence identification, cascading failure and virus propagation. Based on the degree value of the nodes, a method to quickly identify the influence of the nodes, as well as a cascaded failure model considering the local real-time information priority redistribution strategy, is proposed, and a novel metric is proposed to measure the robustness in regard to virus attacks in social networks. Following on from this, the cooperative evolution of network structure and propagation process is studied, and the reliability of adaptive weighted networks is analyzed and discussed