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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Fatma A. Omara
Cairo University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhancing highly-collaborative access control system using a new role-mapping algorithm Doaa Abdelfattah; Hesham A. Hassan; Fatma A. Omara
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2765-2782

Abstract

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, role based access-control (RBAC) Model is deployed in large organizations. The work in this paper is mainly considering the authorization scalability problem, which comes out due to the increase of shared resources and/or the number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with role-to-role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed role-mapping algorithm. A comparative study has been performed to evaluate the performance of the proposed algorithm with concerning the rule-store size and the authorization response time. According to the results, it is found that the proposed algorithm achieves more saving in the number of stored role-mapping rules which minimizes the rule-store size and reduces the authorization response time. Additionally, the RTR model using the proposed algorithm has been implemented by applying a concurrent approach to achieve more saving in the authorization response time. Therefore, it would be suitable in highly-collaborative cloud environments
Utilizing CommonKADS as Problem-Solving and Decision-Making for Supporting Dynamic Virtual Organization Creation Morcous M. Yassa; Hesham A. Hassan; Fatma A. Omara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (223.571 KB) | DOI: 10.11591/ijai.v3.i1.pp1-6

Abstract

Business Opportunity (BO) needs business collaboration and rapid distributed solution. Legacy systems are not enough to cope with it and there is a need to create Dynamic Virtual Organizations (DVO). While ecosystems have no agree in this area of business markets, some earlier DVO work used ecosystems to handle BO. The main objective of this paper is to show how CommonKADS knowledge engineering methodology is used to model DVO; life cycle, identification, and formation. Towards this objective, different perspectives used to analyze Collaboration Network Organization (CNO) have been discussed. Also, four more perspectives (CNO boundary fixing, organizational behavior, CNO federation modeling, and external environments) have been suggested to obtain what we called a Federated CNO Model (FCNOM). We believe that according to the work in this paper, the negotiations within CNO components during its life cycle will be minimized, the DVO configuration automation will be support, and more harmonization between CNO partners will be accomplished.
Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition Alshaimaa M. Mohammed; Samar Shaaban Abdelfattah Haytamy; Fatma A. Omara
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp638-650

Abstract

The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.
Co-Authors Alshaimaa M. Mohammed Doaa Abdelfattah Hesham A. Hassan Morcous M. Yassa Samar Shaaban Abdelfattah Haytamy