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An Effective Node Injection Approach for Attacking Social Network Alignment
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AI
DOI：10.1109/TIFS.2024.3515842.png)
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摘要
En 中文
The importance of social network alignment (SNA) for various downstream applications, such as social network information fusion and e-commerce recommendation, has prompted numerous professionals to develop and share SNA tools. However, malicious actors can exploit these tools to integrate sensitive user information, thereby posing cybersecurity risks. Although many researchers have explored attacking SNA (ASNA) through network modification attacks to protect users, practical feasibility remains challenging. In this study, we propose an effective node injection attack via a dynamic programming framework (DPNIA) to address the problem of modeling and solving ASNA within a limited time and balancing the costs and benefits. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs with greater similarity scores than the pairs between existing nodes, thereby making ASNA solvable. A cross-network evaluation method is employed directly to identify node vulnerabilities, facilitating progressive attacking from easy to difficult. In addition, an optimal injection strategy searching method based on dynamic programming is used to determine which links should be added between the injected and existing nodes, thereby enhancing the effectiveness of the attack at a low cost. Experiments on four real-world datasets demonstrated that DPNIA consistently and significantly surpasses various baselines when attacking both multiple networks simultaneously and a single network.
Keyword:
Social networking (online)
Costs
Dynamic programming
Graph neural networks
Time complexity
Faces
Electronic commerce
Data models
Computer security
Analytical models
Social network alignment
node injection attack
user privacy
dynamic programming
期刊
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8
论文数:
5.2K
被引数:
2.3W
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