Abstract
A number of privacy breaches have occurred in recent years, which has made people pay increased attention to the security of information systems. On the basis of this issue, role-based access control (RBAC) has been proposed and proven through practice to be able to effectively guarantee the security of user system data. But, in RBAC, role engineering is a complex process. To simplify the process, an auxiliary interactive question-and-answer (Q and A) algorithm was proposed based on attribute exploration (machines and humans learn knowledge interactively). the auxiliary interactive Q and A algorithm based on attribute exploration has some defects. It is not only unable to work with many people, but also has difficulty finding qualified Q and A experts in actual work. To address these problems, this paper proposes an attribute exploration-based Role discovery model. This model not only avoids the time-consuming process in role engineering, but also solves the problem of the auxiliary interactive Q and A based on attribute exploration being unable to support multi-person collaborative question–answering. Therefore, the model algorithm can be used for machine learning knowledge to assist people to solve the problem of cross-departmental role formulation.
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References
Alessandro C, Alberto O (2012) Role mining in business: taming role-based access control administration. World Sci. https://doi.org/10.1142/9789814366151_0003
Baader F, Sertkaya B (2004) Applying formal concept analysis to description logics Concept Lattices, Second International Conference on Formal Concept Analysis, ICFCA 2004. 2961:261–286
Bertino E (2003) Security RBAC models—concepts and trends. Comput Secur 22:511–514. https://doi.org/10.1016/S0167-4048(03)00609-6
Chen H-C (2019) Collaboration IoT-based RBAC with trust evaluation algorithm model for massive IoT integrated application. Mobile Netw Appl 24:839–852. https://doi.org/10.1007/s11036-018-1085-0
Cherukuri AK, Jonnalagadda A (2013) Exploring attributes with domain knowledge in formal concept analysis. J Comput Inform Technol 21:109. https://doi.org/10.2498/cit.1002114
Chuan-long Y, Yue-fei Z, Jin-long F, Xin-zheng H (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access J 5:21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418
Deng H, Peng L, Zhang H, Yang B, Chen Z (2019) Ranking-based biased learning swarm optimizer for large-scale optimization. Inf Sci 493:120–137. https://doi.org/10.1016/j.ins.2019.04.037
Downs DD, Rub JR, Kung KC, Jordan CS (1985) Issues in discretionary access control. In: IEEE Symposium on Security and Privacy, 1985. IEEE, pp 208–208. https://doi.org/10.1109/SP.1985.10014
Ferraiolo D, Cugini J, Kuhn DR (1995) Role-based access control (RBAC): features and motivations. In: Proceedings of 11th annual computer security application conference. pp. 241–248
Ganter B, Wille R (2012) Formal concept analysis: mathematical foundations. Springer Science & Business Media, Berlin
Ganter B, Obiedkov S, Rudolph S, Stumme G (2016) Conceptual exploration. Springer. https://doi.org/10.1007/978-3-662-49291-8
Guang-Hui W (1996) Role-based access control models. IEEE Mag J 29:38–47. https://doi.org/10.1109/2.485845
Guigues J-L, Duquenne V (1986) Familles minimales d'implications informatives résultant d'un tableau de données binaires 95:5–18
Ho T (1995) An approach to concept formation based on formal concept analysis. IEICE Trans Inform Syst
Jiang Y, Lin C, Yin H, Tan Z (2004) Security analysis of mandatory access control model. In: IEEE International Conference on Systems. https://doi.org/10.1109/ICSMC.2004.1400987
Lakhal L, Stumme G (2005) Efficient mining of association rules based on formal concept analysis. Lect Notes Comput Sci 3626:180–195
Li J, Mei C, Cherukuri AK, Zhang X (2013) On rule acquisition in decision formal contexts. Intern J Mach Learn Cybernet 4:721. https://doi.org/10.1007/s13042-013-0150-z
Li J, Huang C, Mei C, Yin Y (2016) An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices. Intell Autom Soft Comput 23:1–15. https://doi.org/10.1080/10798587.2016.1212509
Ma J-M, Zhang W-X, Qian Y (2020) Dependence space models to construct concept lattices. Intern J Approx Reason 123:1. https://doi.org/10.1016/j.ijar.2020.04.004
Mi Y, Liu W, Shi Y, Li J (2020) Semi-supervised concept learning by concept-cognitive learning and concept space. IEEE Trans Knowl Data Eng 1–1. https://doi.org/10.1109/TKDE.2020.3010918
Michel MCK, King MC (2019) Cyber influence of human behavior: personal and national security, privacy, and fraud awareness to prevent harm. In: IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/ISTAS48451.2019.8938009
Molloy I et al (2010) Mining roles with multiple objectives. ACM Trans Inf Syst Secur 13:36. https://doi.org/10.1145/1880022.1880030
Obiedkov S, Kourie DG, Eloff JH (2009) Security building access control models with attribute exploration. Comput Secur 28:2–7. https://doi.org/10.1016/j.cose.2008.07.011
Pang Y, Peng L, Chen Z, Yang B, Zhang H (2019) Imbalanced learning based on adaptive weighting and Gaussian function synthesizing with an application on Android malware detection. Inf Sci 484:95–112. https://doi.org/10.1016/j.ins.2019.01.065
Qiu J, Tian Z, Du C, Zuo Q, Su S, Fang BJ (2020) A survey on access control in the age of internet of things. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.2969326
Sandhu RS, Samarati P (1994) Access control: principle and practice. Commun Mag IEEE. https://doi.org/10.1109/35312842
Sandhu R, Ferraiolo D, Kuhn R (2000) The NIST model for role-based access control: towards a unified standard. In: ACM workshop on Role-based access control, vol 344287.344301. https://doi.org/10.1145/344287.344301
Šrndić N, Laskov P (2016) Hidost: a static machine-learning-based detector of malicious files. EURASIP J Info Secur 2016:22. https://doi.org/10.1186/s13635-016-0045-0
Stumme G (1996) Attribute exploration with context implications and exceptions. In: Data analysis and information systems. Springer, Berlin. pp. 457–469. https://doi.org/10.1007/978-3-642-80098-6_39
Stumme G, Taouil R, Bastide Y, Pasquier N, Lakhal L (2001) Intelligent structuring and reducing of association rules with formal concept analysis. In: Annual Conference on Artificial Intelligence. Springer, pp. 335–350. https://doi.org/10.1007/3-540-45422-5_24
Vaidya J, Atluri V, Guo Q (2007) The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM symposium on Access control models and technologies. pp. 175–184. https://doi.org/10.1145/1266840.1266870
Wei L, Liu L, Qi J, Qian T (2019) Rules acquisition of formal decision contexts based on three-way concept lattices. Inform Sci 516:529–544. https://doi.org/10.1016/j.ins.2019.12.024
Zhang L, Xu C, Gao Y, Han Y, Du X, Tian Z (2020) Improved Dota2 lineup recommendation model based on a bidirectional LSTM. Tsinghua Sci Technol 25:712–720. https://doi.org/10.26599/TST.2019.9010065
Acknowledgements
This research was supported by National Natural Science Foundation of China (61701170); Scientific and technological project of Henan Province (Grant No. 202102310340); Foundation of University Young Key Teacher of Henan Province (Grant No. 2019GGJS040, 2020GGJS027); Key scientific research projects of colleges and universities in Henan Province (Grant No. 21A110005).
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Shen, X., Yang, J., Zhang, L. et al. An interactive role learning and discovery model for multi-department RBAC building based on attribute exploration. J Ambient Intell Human Comput 13, 1373–1382 (2022). https://doi.org/10.1007/s12652-020-02634-3
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DOI: https://doi.org/10.1007/s12652-020-02634-3