Automated Framework for Access Control Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6131

Special Issue Editors


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Guest Editor
Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo”, Area della Ricerca CNR di Pisa, Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: access control; model-based specification and testing; security and privacy testing and assessment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo", Area della Ricerca CNR di Pisa, Via G. Moruzzi 1, 56124 Pisa, Italy
Interests: data protection; data protection & compliance; GDPR; access control systems; software testing

Special Issue Information

Dear Colleagues,

The trustworthiness of (personal) data is becoming a stringent need for industrial and research products and solutions. In this context, access control systems are fundamental building blocks for guaranteeing the cybersecurity properties of systems,  ecosystems, and systems of systems. They are the key mechanisms for managing (personal) data and resources access and guaranteeing their privacy and confidentiality, as well as the lawfulness of their processing.

However, correct and compliant access management is an expensive and error-prone process that calls for the adoption of automated solutions and proposals. It also requires a significant reduction of the development effort in terms of time and costs, and an improvement of the accountability, usability, and trustworthiness of access control systems.

Therefore, there is a need for research and industrial activities focused on (automated) facilities and frameworks applicable during all phases of the access control development lifecycle. Specifically, the conceived automated proposals should target the planning, specification, design, implementation, validation and verification, deployment, and maintenance of the access control systems.

The aim of this Special Issue is to gather the latest research and industrial results concerning theories, methodologies, techniques, and new solutions in the access control domain. Researchers, experts, and scholars from both industry and academia are encouraged to present their recent achievements, joint collaborations, and research directions in this area.

Dr. Eda Marchetti
Dr. Said Daoudagh
Guest Editors

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Keywords

  • Lifecycle automation
  • Data protection and privacy
  • Cybersecurity and privacy
  • Modelling and automation
  • Interoperability and context awareness
  • Policy specification, implementation and testing
  • Usability and trustworthiness
  • Accountability and compliance
  • Monitoring and logging
  • Validation and verification
  • Legal aspects
  • Perspectives, challenges, opportunities, and issues in access control automation
  • Specific domains such as:
    • IoT, big data, cloud computing
    • Cyber-physical systems/ecosystems/systems of systems
    • Safety-critical systems/ecosystems/systems of systems
    • Smart environments, automotive, healthcare environments
    • Education and social networks
    • Defense and military
    • etc.

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Published Papers (1 paper)

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Research

20 pages, 2537 KiB  
Article
Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets
by Samer Abdulateef Waheeb, Naseer Ahmed Khan and Xuequn Shang
Electronics 2022, 11(5), 715; https://doi.org/10.3390/electronics11050715 - 25 Feb 2022
Cited by 34 | Viewed by 5412
Abstract
Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news [...] Read more.
Sentiment Analysis (SA) is a technique to study people’s attitudes related to textual data generated from sources like Twitter. This study suggested a powerful and effective technique that can tackle the large contents and can specifically examine the attitudes, sentiments, and fake news of “E-learning”, which is considered a big challenge, as online textual data related to the education sector is considered of great importance. On the other hand, fake news and misinformation related to COVID-19 have confused parents, students, and teachers. An efficient detection approach should be used to gather more precise information in order to identify COVID-19 disinformation. Tweet records (people’s opinions) have gained significant attention worldwide for understanding the behaviors of people’s attitudes. SA of the COVID-19 education sector still does not provide a clear picture of the information available in these tweets, especially if this misinformation and fake news affect the field of E-learning. This study has proposed denoising AutoEncoder to eliminate noise in information, the attentional mechanism for a fusion of features as parts where a fusion of multi-level features and ELM-AE with LSTM is applied for the task of SA classification. Experiments show that our suggested approach obtains a higher F1-score value of 0.945, compared with different state-of-the-art approaches, with various sizes of testing and training datasets. Based on our knowledge, the proposed model can learn from unified features set to obtain good performance, better results than one that can be learned from the subset of features. Full article
(This article belongs to the Special Issue Automated Framework for Access Control Systems)
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