通过对抗拓扰动对动态图的黑盒攻击
Haicheng Tao1, Jie Cao2, Lei Chen3
1College of Information Engineering, Nanjing University of Finance and Economic, 3 Wenyuan Road, Nanjing, 210023, Jiangsu, China.
概括
这项研究引入了一种新的方法,通过添加假节点和链接来攻击动态图. 基于等级强化学习的黑子攻击 (HRBBA) 框架有效地降低了动态图表学习方法的性能.
科学领域:
- 计算机科学 计算机科学
- 图形理论 图形理论
- 机器学习 机器学习
背景情况:
- 对动态图的攻击对于了解信息系统漏洞至关重要.
- 现有的图形重新连接攻击对于现实世界的动态图形来说通常是不切实际的.
研究的目的:
- 提出第一个关于在受限制的黑盒设置中使用对抗拓扰动来攻击动态图的研究.
- 通过将假节点和链接注入到动态图中来开发一种新的攻击策略.
主要方法:
- 提出了一个基于强化学习的等级黑子攻击 (HRBBA) 框架.
- 动态图形扰动被建模为一个连续的决策过程,有三个子任务.
- 一个不可察觉的扰动约束被用于攻击隐藏.
- HRBBA框架是使用一个演员-关键过程进行优化.
主要成果:
- HRBBA框架显著降低了各种动态图表学习方法的性能.
- 在四个现实世界的动态图表上进行了实验.
- 用于链接预测,节点分类和网络集群的受害者方法受到重大影响.
结论:
- 拟议的HRBBA框架提供了一种有效的方法来攻击黑盒设置中的动态图.
- 注入假节点和链接是对对抗拓扰动的可行策略.
- HRBBA攻击证明了当前动态图形学习方法对复杂攻击的脆弱性.
相关概念视频
01:19Survival Tree
87
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
87
02:26Protein Networks
4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K