Sustainable Security Solutions for Mobile Applications with Symmetry
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2462
Special Issue Editors
Interests: program analysis; software evolution; machine learning for software engineering; software security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With mobile software applications increasingly affecting our lives and societies, their security and privacy issues have received growing attention in the research community, particularly techniques utilizing data-driven, notably machine/deep learning, methods having demonstrated tremendous potential in recent years, a major challenge being that such solutions tend to not be sustainable because of the moving defense targets; that is, a technique devised to work effectively for one population of mobile applications developed in a certain period of time may not work well for applications coming out later in time, because of the constant evolution of the population. This Special Issue is dedicated to exploring and discovering more sustainable novel solutions so that we can eliminate the need to repeatedly and frequently develop/update techniques for newer mobile application populations; in fact, even if it is affordable to do so, we may not have newer samples available for re-training/updating the previously trained learning models. The topic pursues a synergy between sustainable mobile software security and robust data-driven (especially AI/ML) models, where the symmetry concept is reflected between sustainability in the former and robustness in the latter, this Special Issue collecting papers that highlight the recent advances and broad research efforts tackling the challenge of technique deterioration, the lack of training samples that represent new/emerging software applications, and the subsequent proliferation of zero-day security breaches.
Dr. Haipeng Cai
Dr. Jia-Ning Luo
Guest Editors
Manuscript Submission Information
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Keywords
- data-driven technique
- mobile security
- sustainability
- data evolution
- deterioration
- robustness
- adversarial samples
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