Usability, Security and Machine Learning

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 6227

Special Issue Editors


E-Mail Website
Guest Editor
School of Mathematical and Computer Science, Heriot Watt University, Edinburgh EH14 4AS, UK
Interests: computer science; machine learning; knowledge management; big data
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Mathematical and Computer Science, Heriot Watt University, Edinburgh EH14 4AS, UK
Interests: digital forensics; information security; computer vision; data visualisation & analytics

E-Mail Website
Guest Editor
Institute of Management Sciences, Peshawar, Pakistan
Interests: software engineering; search-based software engineering; intelligent systems

Special Issue Information

Dear Colleagues,

In recent years, information security and usability have been intensely contributing to the software industry.  While security is important to ensure confidentially, integrity, and availability of information, usability is essential for providing end-users with a satisfying experience. According to ISO 9241, usability is the effectiveness, efficiency, and satisfaction with which specified users achieve specified goals in a particular context. With the exponential growth of mobile technology and the web, usability has become central to enhanced user engagement and increased user satisfaction. This directly influences the business goals.

While usability is key to better user experience, information security provides the core principles to ensure the security of systems.  Information security is the protection of information and systems from unauthorized access and use, disclosure, disruption, modification, perusal, inspection, recording or destruction in order to uphold the information security principles. The concept of information security is formed from the recognition that “information” is valuable and that it requires protection. According to ISO/IEC 27002, it is the protection of information from a wide range of threats that ensures business continuity and minimizes business risks. With the advent of the Internet and the increased use of web applications, the reliance of many businesses on the security of their information has been growing exponentially.  A number of techniques has been used to protect against security threats. The use of machine learning as a security model has been increasing. Machine learning is a broad subfield of computational intelligence that is concerned with the development of techniques that allow computers to “learn”. With the increased and effective use of machine learning techniques across different areas in computer science, there is an increased use of this approach to identify and mitigate security threats.

Thus, to cope with the rising demands of usability and ensure security of information assets alike, there is a need for developing secure but usable applications. The objective of this Special Issue is to present studies in the field of human–computer interaction, interaction design, usability, information security, usable security, and machine learning for security and cyber security.  Therefore, researchers are invited to submit their manuscripts to this Special Issue and contribute their models, proposals, reviews, and studies.

Technical Program Committee Member:

Associate Professor Babar Shah College of Technological Innovation, Zayed University, Abu Dhabi, UAE

Dr. Abrar Ullah
Dr. Ryad Soobhany
Dr. Sajid Anwar
Dr. Imran Razzak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • usability
  • human–computer interaction
  • interaction design
  • security
  • information security
  • usable security
  • machine learning
  • deep learning

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1232 KiB  
Article
Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning
by Robertas Damaševičius and Ligita Zailskaitė-Jakštė
Electronics 2022, 11(3), 400; https://doi.org/10.3390/electronics11030400 - 28 Jan 2022
Cited by 4 | Viewed by 2734
Abstract
The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure [...] Read more.
The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework. Full article
(This article belongs to the Special Issue Usability, Security and Machine Learning)
Show Figures

Figure 1

17 pages, 504 KiB  
Article
Understanding Coding Behavior: An Incremental Process Mining Approach
by Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Domenico Redavid and Stefano Ferilli
Electronics 2022, 11(3), 389; https://doi.org/10.3390/electronics11030389 - 27 Jan 2022
Cited by 4 | Viewed by 2242
Abstract
Capturing and analyzing interaction data in real-time from development environments can help in understanding how programmers handle coding activities. We propose the use of process mining to learn coding behavior from event logs captured from a customized Integrated Development Environment, concerning interactions with [...] Read more.
Capturing and analyzing interaction data in real-time from development environments can help in understanding how programmers handle coding activities. We propose the use of process mining to learn coding behavior from event logs captured from a customized Integrated Development Environment, concerning interactions with both such an environment and a Version Control System. In particular, by using an incremental approach, the discovered model can be refined after every single development session, which avoids the need to for the model to learn from scratch from previous sessions. It would also allow one to provide the programmer timely suggestions to improve their performance. In this paper, we applied off-line incremental behavior, so as to be able to analyze it at several levels of depth and at different moments. As a preliminary evaluation of our approach, we investigated the coding activities of six novice students of a Java academic programming course working on a programming case study. The results provide some useful information about the initial difficulties in coding activities faced by programmers and show that their coding behavior could be considered as “formed” after a development task requiring approximately 4000 rows of code. Full article
(This article belongs to the Special Issue Usability, Security and Machine Learning)
Show Figures

Figure 1

Back to TopTop