Survey on Access Control Models Feasible in Cyber-Physical Systems

2018 ◽  
pp. 103-152 ◽  
Author(s):  
Mikel Uriarte ◽  
Jasone Astorga ◽  
Eduardo Jacob ◽  
Maider Huarte ◽  
Oscar López
Keyword(s):  
Access Control ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Access Control Models ◽  
Control Models

scholarly journals CHERI Macaroons: Efficient, host-based access control for cyber-physical systems

2020 ◽  
Author(s):  
Michael Dodson ◽  
Alastair R. Beresford ◽  
Alexander Richardson ◽  
Jessica Clarke ◽  
Robert N. M. Watson
Keyword(s):  
Access Control ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

On the Expressive Power of Negated Conditions and Negative Authorizations in Access Control Models

2021 ◽  
pp. 102586
Author(s):  
Padmavathi Iyer ◽  
Amirreza Masoumzadeh ◽  
Paliath Narendran
Keyword(s):  
Access Control ◽  
Expressive Power ◽  
Access Control Models ◽  
Control Models

Access Control Models for Online Social Networks

2014 ◽  
pp. 451-484
Author(s):  
Rula Sayaf ◽  
Dave Clarke
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Online Social Networks ◽  
Personal Data ◽  
Security And Privacy ◽  
Open Problems ◽  
Access Control Models ◽  
Control Models ◽  
Underlying Mechanisms ◽  
High Level

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.

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