Agent-based access control framework for enterprise content management

2021 ◽  
Vol 17 (2) ◽  
pp. 129-143
Author(s):  
Nadia Hocine
Keyword(s):  
Access Control ◽  
Well Being ◽  
Content Management ◽  
Agent Based ◽  
Open Network ◽  
Access Control Models ◽  
Control Models ◽  
Enterprise Content Management ◽  
Control Framework ◽  
New Devices

Telework is an important alternative to work that seeks to enhance employees’ safety and well-being while reducing the company costs. Employees can work anytime, any where and under high mobility conditions using new devices. Therefore, the access control of remote exchanges of Enterprise Content Management systems (ECM) have to take into consideration the diversity of users’ devices and context conditions in a telework open network. Different access control models were proposed in the literature to deal with the dynamic nature of users’ context and devices. However, most access control models rely on a centralized management of permissions by an authorization entity which can reduce its performance with the increase of number of users and requests in an open network. Moreover, they often depend on the administrator’s intervention to add new devices’ authorization and to set permissions on resources. In this paper, we suggest a distributed management of access control for telework open networks that focuses on an agent-based access control framework. The framework uses a multi-level rule engine to dynamically generate policies. We conducted a usability test and an experiment to evaluate the security performance of the proposed framework. The result of the experiment shows that the ability to resist deny of service attacks over time increased in the proposed distributed access control management compared with the centralized approach.

Access Control Framework for Cloud Computing

2019 ◽  
pp. 698-711
Author(s):  
Kashif Munir ◽  
Lawan A. Mohammed
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Role Based Access Control ◽  
Discretionary Access Control ◽  
Job Functions ◽  
Access Control Models ◽  
Control Models ◽  
Control Framework ◽  
Role Based ◽  
Access Privileges

Access control is generally a rule or procedure that allows, denies, restricts or limit access to system's resources. It may, as well, monitor and record all attempts made to access a system. Access Control may also identify users attempting to access unauthorized resources. It is a mechanism which is very much important for protection in computer security. Various access control models are in use, including the most common Mandatory Access Control (MAC), Discretionary Access Control (DAC) and Role Based Access Control (RBAC). All these models are known as identity based access control models. In all these access control models, user (subjects) and resources (objects) are identified by unique names. Identification may be done directly or through roles assigned to the subjects. These access control methods are effective in unchangeable distributed system, where there are only a set of Users with a known set of services. For this reason, we propose a framework which is well suited to many situations in cloud computing where users or applications can be clearly separated according to their job functions. In this chapter, we proposes a role based access control framework with various features including security of sensitive data, authorization policy and secure data from hackers. Our proposed role based access control algorithm provides tailored and fine level of user access control services without adding complexity, and supports access privileges updates dynamically when a user's role is added or updated.

Access Control Framework for Cloud Computing

2015 ◽  
pp. 314-325
Author(s):  
Kashif Munir ◽  
Lawan A. Mohammed
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Role Based Access Control ◽  
Discretionary Access Control ◽  
Job Functions ◽  
Access Control Models ◽  
Control Models ◽  
Control Framework ◽  
Role Based ◽  
Access Privileges

Access control is generally a rule or procedure that allows, denies, restricts or limit access to system's resources. It may, as well, monitor and record all attempts made to access a system. Access Control may also identify users attempting to access unauthorized resources. It is a mechanism which is very much important for protection in computer security. Various access control models are in use, including the most common Mandatory Access Control (MAC), Discretionary Access Control (DAC) and Role Based Access Control (RBAC). All these models are known as identity based access control models. In all these access control models, user (subjects) and resources (objects) are identified by unique names. Identification may be done directly or through roles assigned to the subjects. These access control methods are effective in unchangeable distributed system, where there are only a set of Users with a known set of services. For this reason, we propose a framework which is well suited to many situations in cloud computing where users or applications can be clearly separated according to their job functions. In this chapter, we proposes a role based access control framework with various features including security of sensitive data, authorization policy and secure data from hackers. Our proposed role based access control algorithm provides tailored and fine level of user access control services without adding complexity, and supports access privileges updates dynamically when a user's role is added or updated.

Access Control Models in Multiuser Scientometric Content Management Systems

2016 ◽  
Vol 7 (12) ◽  
pp. 547-558 ◽  
Author(s):  
V. A. Vasenin ◽  
◽  
A. A. Itkes ◽  
V. Yu. Bukhonov ◽  
A. V. Galatenko ◽  
...  
Keyword(s):  
Access Control ◽  
Content Management ◽  
Management Systems ◽  
Access Control Models ◽  
Control Models ◽  
Content Management 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.

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