Trustworthy Machine Learning for Network and System Security

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue entitled "Trustworthy Machine Learning for Network and System Security". This Special Issue delves into the critical intersection of artificial intelligence (AI) and security, particularly concerning circuits and systems that hold pivotal roles in modern society. Highlighting advancements in trustworthy AI technologies, this Special Issue emphasizes the imperative of ensuring reliability and security in AI-infused systems. Trustworthy machine learning is indispensable for fortifying network and system security. It ensures reliable threat detection, resilience against adversarial attacks, data integrity, privacy protection, and model transparency. By continuously monitoring and adapting to evolving threats, prioritizing ethical considerations, and fostering collaborative defense mechanisms, trustworthy machine learning safeguards against vulnerabilities and reinforces the resilience of digital infrastructures.

This Special Issue aims to provide a leading-edge forum to foster interactions between researchers and developers and the cybersecurity and AI communities, giving attendees an opportunity to interact with experts in academia, industry, and the government.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Machine learning/AI for the following:
  • Network security;
  • System security, data security and privacy;
  • IoT and Industry 4.0/5.0;
  • Malware, anomalies, and intrusion detection.
• Adversarial machine learning and the robustness of AI models against malicious actions.
• Privacy inference attacks against deep learning systems, e.g., membership inference, model extraction, and model inversion.
• The interpretability and explainability of machine learning models in cybersecurity.
• Privacy-preserving machine learning.
• Trustworthy machine learning.

We look forward to receiving your contributions.  

Dr. Depeng Xu
Dr. Shuhan Yuan
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.

• machine learning
• trustworthy AI
• network security
• system security
• threat detection
• adversarial attack
• data privacy
• transparency and interpretability