工具/数据集论文:使用条件表格GAN生成现实ABAC数据

Ritwik Rai, S. Sural
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引用次数: 0

摘要

基于属性的访问控制(ABAC)越来越多地用于各种应用,包括云服务、物联网、智能家居、医疗保健等。然而,用基准现实数据集进行系统和可重复的实验仍然是一个挑战。为了解决这个缺点,在本文中,我们引入了一种称为ConGRASS(现实ABAC仿真研究的条件表格GAN)的方法来生成大型ABAC数据集。从给定的(可能)有限大小的真实世界数据集开始,我们首先训练一个条件表格生成对抗网络来学习其分布。训练后的模型用于生成与原始数据集分布相似的任意大尺寸的真实数据集。ConGRASS已经被实现为一个免费使用的基于web的工具,用户可以选择列出的真实数据集的名称以及所需的数据集大小。生成一个包含ABAC数据的CSV文件作为输出。广泛的评估表明,该模型能够忠实地学习所选真实数据的统计特性。当将这样的数据集用于实际问题时,性能得到了显着提高,证明了ConGRASS的实用性。
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Tool/Dataset Paper: Realistic ABAC Data Generation using Conditional Tabular GAN
Attribute-based Access Control (ABAC) is increasingly being used in a wide variety of applications that include cloud services, IoT, smart homes, healthcare and several others. Conducting systematic and reproducible experiments with benchmark realistic datasets, however, still remains a challenge. To address this shortcoming, in this paper we introduce a method called ConGRASS (Conditional Tabular GAN for Realistic ABAC Simulation Studies) for generating large ABAC datasets. Starting with a given real world dataset of (potentially) limited size, we first train a conditional tabular generative adversarial network for learning its distribution. The trained model is used to generate realistic datasets of arbitrarily large sizes having distribution similar to the original dataset. ConGRASS has been implemented as a free to use web-based tool in which a user can choose the name of a listed real dataset along with the desired dataset size. A CSV file containing ABAC data is generated as output. Extensive evaluation shows the ability of the model to faithfully learn the statistical properties of the selected real data. When such a dataset is used in an actual problem, significant improvement in performance is achieved, proving the utility of ConGRASS.
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