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Article

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities

1
Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
2
University Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan
3
Department of Computer Science, Federal Urdu University of Arts, Science &Technology, Islamabad 45570, Pakistan
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Department of Health Administration, Governors State University, Chicago’s Southland, University Park, IL 60484, USA
5
School of Computer Science and Engineering, Sacred Heart University, 3135 Easton Turnpike, Fairfield, CT 06825, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Jorge Bernal Bernabe, Antonio Skarmeta, Fabio Massacci and Davy Preuveneers
Sensors 2021, 21(13), 4253; https://doi.org/10.3390/s21134253
Received: 10 May 2021 / Revised: 26 May 2021 / Accepted: 28 May 2021 / Published: 22 June 2021
(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Cities)
Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles. View Full-Text
Keywords: access control; intelligent RBAC; multi-domain collaboration; dynamic environments; smart city applications; semantic role mining; ontology; multi-agent system; word embedding; LSTM access control; intelligent RBAC; multi-domain collaboration; dynamic environments; smart city applications; semantic role mining; ontology; multi-agent system; word embedding; LSTM
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MDPI and ACS Style

Ghazal, R.; Malik, A.K.; Raza, B.; Qadeer, N.; Qamar, N.; Bhatia, S. Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities. Sensors 2021, 21, 4253. https://doi.org/10.3390/s21134253

AMA Style

Ghazal R, Malik AK, Raza B, Qadeer N, Qamar N, Bhatia S. Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities. Sensors. 2021; 21(13):4253. https://doi.org/10.3390/s21134253

Chicago/Turabian Style

Ghazal, Rubina, Ahmad K. Malik, Basit Raza, Nauman Qadeer, Nafees Qamar, and Sajal Bhatia. 2021. "Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities" Sensors 21, no. 13: 4253. https://doi.org/10.3390/s21134253

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