The rapid development of Internet technology has made social networks central platforms for information dissemination and acquisition. As key participants, users exhibit increasingly complex connection patterns, reflecting the dynamic nature of online interactions. Therefore, in order to better understand and manage social networks, analyzing these connection patterns, particularly identifying the potential connections between users with similar tendencies, has become a critical research focus in social network studies. Nevertheless, the existing methods exhibit limitations in comprehensively harnessing the heterogeneous nature of social networks and usually over-rely on local network structures while neglecting global patterns. To address these problems, we propose an innovative method based on weighted random walks within heterogeneous information networks (HINs). We first employ HINs to structurally represent and systematically organize complex social network data, leveraging meta-paths to model user connection patterns at semantic levels. Then, based on the meta-paths, we develop an adaptive weighted random walk strategy to integrate global structural features with local semantic information and connect users with similar tendencies. Experimental results on both Twitter and public HIN datasets demonstrate that our method outperforms other classical methods in accurately connecting users with similar tendencies.

Adversarial SQL injection (SQLi) refers to the process in which attackers dynamically modify their attack strategies based on feedback from the target Web Application Firewall (WAF) in an attempt to bypass it. The emergence of automated adversarial SQLi tools in recent years, such as AdvSQLi, RAT, and GPTFuzzer, demonstrates that adversarial SQLi has become a critical method for vulnerability detection and SQLi execution. These tools' intelligent nature poses a significant threat to existing SQLi defense mechanisms. Payloads subjected to adversarial mutation can effectively deceive machine learning-based WAFs, while rule-based WAFs are more susceptible to having vulnerabilities discovered. To address this issue, we propose a hybrid model based on enhanced Character-Level CNN (CLCNN) and Reinforcement Learning (RL), which detects malicious patterns through multi-scale feature extraction by CLCNN from a pattern-matching perspective. Additionally, the reinforcement learning module is employed for adversarial training, executing de-obfuscation operations on payloads for which the CLCNN outputs a low confidence level, further processing ambiguous payloads. The experimental results indicate that even without a priori knowledge, CLCNN demonstrates strong resistance to such tools. And the RL module can further enhance Recall through adversarial training, with minimal reduction in the hybrid model's performance on standard SQLi datasets.

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.

A complex network is a fundamental tool to describe real-world complex systems, with most real-world systems containing multiple object types and relationships that can be described as heterogeneous information networks. However, with the increasing network complexity, understanding the complex patterns and finding the meta paths or meta-structures of the heterogeneous information networks has become challenging. This paper proposes a sparse representation for heterogeneous information networks and extracts the heterogeneous information atoms that describe the basic connection pattern of the original heterogeneous information network. The heterogeneous information atoms help extract the main meta-paths or meta-structures and understand the complex patterns of the original heterogeneous information network. Furthermore, the heterogeneous information networks can be decomposed, dimension-reduced, and reconstructed through the heterogeneous information atoms. Extensive experimental results demonstrate that heterogeneous information atoms and sparse coding represent the basic connection pattern of real-world heterogeneous information networks. Indeed, the developed method can reconstruct a network with a recovery exceeding 90%.

With the ubiquity of social media, privacy leakage has become a urgent problemfor social media managers. Studying how the privacy information diffuses through social media has attracted much attention. As a prerequisite, modeling privacy information diffusion is important research. Current approaches for modeling information diffusion are not available for privacy information since they did not consider the propagation features of privacy information in social media. Thispaper discusses the problem of modeling privacy information in social media and its challenges. We first analyse the information diffusion paths in the basic parameters of complex network and the high-order structures. We find that the privacy information is different in propagation features and the size of star structures. Second, a new information diffusion model is illustrated to simulate the diffusion process of information in social media by considering the following three parameters: 1) the probability of users receiving this message, 2) the probability that users have a tendency to forward this message and 3) the interest the users hold for this message. Finally, a block mechanism is designed to congest the diffusion of privacy information in social media.

Social media has become a ubiquitous tool for spreading news, messages, and generally allowing for communication between individuals. Hence, studying how our privacy information might also spread across social media is important research. To date, many studies have used information diffusion models to simulate and then examine how information flows through social networks. But these models are theoretical, and newsworthy information may not behave in the same way as privacy information, raising the question: Are the observed phenomena indicative of real privacy propagation? To explore this question, we assembled a dataset from Twitter comprising propagated information flows for both private and normal information. We then built a graph convolutional network to trace and classify differences in the way each type of information spreads throughout the platform. The results reveal that there are indeed key differences in the diffusion processes of the two types of information. More importantly, we design privacy-preserving methods to reduce the privacy propagation in social media.