Identification of network vulnerability is one of the important means of cyberspace operation, management and security. As a typical case of network vulnerability, network cascading failures are often found in infrastructure networks such as the power grid system, communication network and road traffic, where the failure of a few nodes may cause devastating disasters to the whole complex system. Therefore, it is very important to identify the critical nodes in the network cascading failure and understand the internal laws of cascading failure in complex systems so as to fully grasp the vulnerability of complex systems and develop a network management strategy. The existing models for cascading failure analysis mainly evaluate the criticality of nodes by quantifying their importance in the network structure. However, they ignore the important load, node capacity and other attributes in the cascading failure model. In order to address those limitations, this paper proposes a novel critical node identification method in the load network from the perspective of a network adversarial attack. On the basis of obtaining a relatively complete topology, first, the network attack can be modeled as a cascading failure problem for the load network. Then, the concept of load percolation is proposed according to the percolation theory, which is used to construct the load percolation model in the cascading failure problem. After that, the identification method of critical nodes is developed based on the load percolation, which accurately identifies the vulnerable nodes. The experimental results show that the load percolation parameter can discover the affected nodes more accurately, and the final effect is better than those of the existing methods.

Rumor propagation is an important issue in online social networks (OSNs), while modelling rumor propagation is critical and challenging. Existing studies are restrictive for unrealistic assumptions. We find that the inherent property of a rumor has determinant impact on propagation dynamics, and rumor propagates across different clustered communities with variable transmissibility. In this letter, we formulate the problem using information theory and establish a rigorous two layer epidemic model. As a result, the proposed model reflects the studied subject better than the previous models. Extensive experiments have been conducted through real-world twitter dataset, the results confirm our theoretical findings, and demonstrate that the proposed model outperforms the existing ones.

In this letter, we propose a novel network topology measurement method called the neural network gradient-guided method (NGM), which can use traceroutes to efficiently collect topological information (IPs and links) by selecting appropriate destination IPs. In this method, the mapping between probing destination IPs and probing revenues is modelled into a multiclassification problem using a neural network model, and networks are iteratively probed under the guidance of the neural network gradients. Experimental results in real networks demonstrated that NGM can collect more IPs and links by comparing with the methods that randomly selects IPs and subnets for probing.

Internet Autonomous System (AS) level topology includes AS topology structure and AS business relationships, describes the essence of Internet inter-domain routing, and is the basis for Internet operation and management research. Although the latest topology inference methods have made significant progress, those relying solely on local information struggle to eliminate inference errors caused by observation bias and data noise due to their lack of a global perspective. In contrast, we not only leverage local AS link features but also re-examine the hierarchical structure of Internet AS-level topology, proposing a novel inference method called topoKG. TopoKG introduces a knowledge graph to represent the relationships between different elements on a global scale and the business routing strategies of ASes at various tiers, which effectively reduces inference errors resulting from observation bias and data noise by incorporating a global perspective. First, we construct an Internet AS-level topology knowledge graph to represent relevant data, enabling us to better leverage the global perspective and uncover the complex relationships among multiple elements. Next, we employ knowledge graph meta paths to measure the similarity of AS business routing strategies and introduce this global perspective constraint to infer the AS business relationships and hierarchical structure iteratively. Additionally, we embed the entire knowledge graph upon completing the iteration and conduct knowledge inference to derive AS business relationships. This approach captures global features and more intricate relational patterns within the knowledge graph, further enhancing the accuracy of AS-level topology inference. Compared to the state-of-the-art methods, our approach achieves more accurate AS-level topology inference, reducing the average inference error across various AS link types by up to 1.2 to 4.4 times.