Privacy-Preserving and System Security Control Based on Machine Learning
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 1048
Special Issue Editors
Interests: cybersecurity; industrial control system security; personal identification methods; Industry 4.0 technologies
Special Issues, Collections and Topics in MDPI journals
Interests: security; cryptographic protocols; anomaly/intrusion detection; reverse engineering; AI/ML/DL
Special Issues, Collections and Topics in MDPI journals
Interests: networked-embedded sensing; information processing; control engineering; building automation; smart city; data analytics; computational intelligence; industry and energy applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The widespread use of information and communication technologies has revolutionized our lives, offering unprecedented convenience and connectivity. However, this progress has also introduced significant challenges to privacy preservation and system security. Machine learning techniques have emerged as powerful tools capable of addressing the evolving security and privacy challenges.
This Special Issue on “Privacy-Preserving and System Security Control Based on Machine Learning” invites original research contributions and review articles to explore the latest advancements and applications of machine learning for privacy preservation and system security. We welcome submissions that cover a broad range of topics, including but not limited to the following:
- Privacy-preserving machine learning algorithms;
- Differential privacy;
- Adversarial machine learning for security control;
- Anomaly detection using machine learning for system security;
- Zero-day attack detection using machine learning;
- Secure federated learning;
- Privacy-enhancing technologies in machine learning.
We expect this Special Issue to provide a timely and significant platform for researchers and practitioners to present their latest findings and foster collaborations in this swiftly advancing field. We are looking forward to your contributions.
Dr. Emil Pricop
Dr. Jaouhar Fattahi
Dr. Grigore Stamatescu
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. Applied Sciences 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
- privacy preserving
- system security
- machine learning
- differential privacy
- adversarial machine learning
- secure federated learning
- machine learning based attack detection
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