Context-Aware Risk Attribute Access Control

Author:

Li Binyong1234,Yang Fan1,Zhang Shaowei1

Affiliation:

1. School of Cybersecurity, Xin Gu Industrial College, Chengdu University of Information Technology, Chengdu 610225, China

2. Zhejiang Geely Holding Group, Hangzhou 310051, China

3. Advanced Cryptography and System Security Key Laboratory of Sichuan Province, Chengdu 610225, China

4. SUGON Industrial Control and Security Center, Chengdu 610225, China

Abstract

Traditional access control systems exhibit limitations in providing flexible authorization and fine-grained access in the face of increasingly complex and dynamic access scenarios. This paper proposes a context-aware risk access control model to address these challenges. By developing a multi-level contextual risk indicator system, the model comprehensively considers real-time contextual information associated with access requests, dynamically evaluates the risk level of these requests, and compares the outcomes with predefined risk policies to facilitate access decisions. This approach enhances the dynamism and flexibility of access control. To improve the accuracy and reliability of risk assessments, we propose a combination weighting method grounded in game theory. This method reconciles subjective biases and the limitations of objective data by integrating both subjective and objective weighting techniques, thus optimizing the determination process for risk factor weights. Furthermore, smart contracts are introduced to monitor user behavior during access sessions, thereby preventing malicious attacks and the leakage of sensitive information. Finally, the model’s performance and authorization granularity are assessed through empirical experiments. The results indicate that the model effectively addresses the requirements of dynamic and fine-grained access scenarios, improving the system’s adaptability to risk fluctuations while safeguarding sensitive information.

Funder

the Sichuan Science and Technology Program

Publisher

MDPI AG

Reference20 articles.

1. Atlam, H.F., Azad, M.A., and Alassafi, M.O. (2020). Risk-based access control model: A systematic literature review. Future Internet, 12.

2. Cha, S.C., Hsuan, Y.H., and Yeh, K.H. (November, January 26). An Evolutionary Risk-based Access Control Framework for Enterprise File Systems. Proceedings of the IEEE 8th World Forum on Internet of Things (WF-IoT), Yokohama, Japan.

3. A privacy risk assessment scheme for fog nodes in access control system;Ke;IEEE Trans. Reliab.,2021

4. Atlam, H.F., Azad, M.A., and Fadhel, N.F. (2022). Efficient NFS model for risk estimation in a risk-based access control model. Sensors, 22.

5. Dynamic risk-based decision methods for access control systems;Shaikh;Comput. Secur.,2012

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