A heterogeneous graph-based semi-supervised learning framework for access control decision-making
Yin, Jiao ORCID: 0000-0002-0269-2624, Chen, Guihong ORCID: 0000-0002-0760-7011, Hong, Wei ORCID: 0000-0003-2833-9228, Cao, Jinli ORCID: 0000-0002-0221-6361, Wang, Hua ORCID: 0000-0002-8465-0996 and Miao, Yuan ORCID: 0000-0002-6712-3465 (2024) A heterogeneous graph-based semi-supervised learning framework for access control decision-making. World Wide Web, 27 (4). ISSN 1386-145X
Abstract
For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security. This paper proposes a novel semi-supervised learning framework that leverages heterogeneous graph neural network-based embedding to encapsulate both the intricate relationships within the organizational structure and interactions between users and resources. Unlike existing methods focusing solely on individual user and resource attributes, our approach embeds organizational and operational interrelationships into the hidden layer node embeddings. These embeddings are learned from a self-supervised link prediction task based on a constructed access control heterogeneous graph via a heterogeneous graph neural network. Subsequently, the learned node embeddings, along with the original node features, serve as inputs for a supervised access control decision-making task, facilitating the construction of a machine-learning access control model. Experimental results on the open-sourced Amazon access control dataset demonstrate that our proposed framework outperforms models using original or manually extracted graph-based features from previous works. The prepossessed data and codes are available on GitHub,facilitating reproducibility and further research endeavors.
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Item type | Article |
URI | https://vuir.vu.edu.au/id/eprint/48717 |
DOI | 10.1007/s11280-024-01275-2 |
Official URL | http://dx.doi.org/10.1007/s11280-024-01275-2 |
Subjects | Current > FOR (2020) Classification > 4602 Artificial intelligence Current > FOR (2020) Classification > 4605 Data management and data science Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
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