AI-Driven Security for Mobile and Distributed Computing Environments

Dear Colleagues,

Current advances in 5G/6G mobile technilogy and the proliferation IoT, cloud and edge computing is driving the development of innovative services and applications. However, the range of cyber security threats against mobile, distributed and cloud systems has also expanded.  The cyberattacks have become more sophisticated and harder to detect and thwart as malicious actors use covert channels to penetrate and jeopradise defence systems based on artificial intelligence (AI) models. There is a need to develop novel approaches and mechanisms capable of dealing with the ever evolving cyber threat landscape and cyberattack channel diversity.

This Special Issue seeks to publish cutting-edge research that leverages AI to enhance the security of mobile, distributed and cloud service environments across their security lifecycle: detection, response and recovery. We invite original research and review articles that explore how machine-learning/deep-learning (ML/DL) algorithms and AI technologies can be applied to build intelligent, adaptive and explainable security defenses for next-generation mobile and distributed systems and services.

Topics of interest include, but are not limited to, the following:

• AI for Threat Detection and Defense: Advanced anomaly detection in mobile networks (5G/6G), IpT, cloud and edge computing and defending ML/DL models against adversarial attacks.
• AI-Powered Security Automation: Automated security testing and vulnerability assessment for mobile apps and AI-driven security orchestration, automation and response (SOAR) for mobile systems.
• Privacy and Trust: AI-federated learning and privacy-preserving techniques in the mobile cloud, explainable AI (XAI) for decisiion support in mobile and distibuted contexts.
• Intelligent Access and Edge Security: AI-enhanced authentication for mobile/IoT devices, securing mobile edge computing, and V2X and UAV communication.
• Emerging Paradigms: Applications of Large Language Models (LLMs) in mobile threat intelligence, code analysis and social engineering defense.

We welcome submissions of theoretical and applied work, including empirical research and literature surveys, from both academia and industry. Submissions should clearly demonstrate how AI-driven methods, models and solutions enhance the security, robustness or privacy of mobile services and systems. Interdisciplinary contributions integrating cybersecurity, network engineering and mobile computing perspectives are highly encouraged.

Dr. Krassie Petrova
Prof. Dr. Jiguo Li
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly 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 1800 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.