Unstructured Text Resource Access Control Attribute Mining Technology Based on Convolutional Neural Network

Abstract

In the attribute-based access control (ABAC) model, attributes are the basis for controlling access to data resources. The existing attribute extraction methods that are based on manual management are time consuming and have a high cost, variable accuracy, and poor scalability when dealing with massive unstructured text from big data resources. This paper proposes a multidimensional hybrid feature generation method for text resource attributes. The method comprehensively calculates the characteristics of attributes themselves, the relationships between attributes, and the relationship between attributes and resources. It can fully and accurately characterize the attributes. It converts attribute features into grayscale images in order to translate attribute mining problems into image recognition problems. We propose an attribute mining method based on a convolutional neural network (CNN). We use neural networks to automatically correlate features. It means there is no need to manually consider the importance of features and their relationships. This avoids the need for security experts to manually label the attributes of massive resources and facilitates the automatic and intelligent mining of ABAC resource attributes. The experimental results show that compared with the benchmark algorithm, the proposed method has improved accuracy and recall rate and can provide attribute support for ABAC of big data resources.

Keywords

• Attribute-based access control (ABAC)
• attribute mining
• deep learning
• unstructured data