New Trends in Big Data, Artificial Intelligence and Data Security
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 39
Special Issue Editors
Interests: system security; artificial intelligence
Interests: AI security; network system security; computer vision security
Special Issue Information
Dear Colleagues,
The explosive growth of artificial intelligence (AI), particularly deep learning (DL), presents a transformative yet complex landscape for cybersecurity. This Special Issue explores the dual frontier at the intersection of AI and security: understanding the vulnerabilities inherent to AI systems themselves and harnessing AI’s power to build more resilient network defenses. We solicit research addressing fundamental weaknesses in AI/ML systems. Key areas include developing robust defenses against adversarial attacks, preserving privacy during model training/inference, and detecting backdoors in compromised models. Furthermore, we also seek innovative applications leveraging AI to significantly enhance network security. This encompasses advanced threat detection, building adaptive systems for automated response and policy optimization, employing AI for automated vulnerability discovery and risk assessment, and creating efficient security solutions for large-scale environments (cloud, IoT, critical infrastructure).
This Special Issue aims to showcase cutting-edge research that pushes the boundaries of securing AI and leveraging AI for security, fostering a safer and more trustworthy digital future. We welcome contributions tackling cross-cutting themes such as scalability, efficiency, robustness guarantees, and ethical considerations in deployed AI security systems.
Topics include, but are not limited to, the following: adversarial machine learning; privacy-preserving AI; explainable AI (XAI) for security; federated learning security; AI-enabled IDS/IPS; malware analysis using DL; phishing/spam detection; network traffic analysis with AI; AI for vulnerability management, security in large language models (LLMs); robust and trustworthy AI deployments.
Dr. Tiantian Zhu
Dr. Yinbo Yu
Dr. Xue Leng
Guest Editors
Manuscript Submission Information
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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. Mathematics 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 2600 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
- deep learning
- adversarial attacks
- backdoor attacks
- federated learning security
- malware analysis
- large language models
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