Aims & Scope

Aims

The Journal of Machine Learning and Information Security (JMLIS) aims to be one of the world's premier journals for disseminating cutting-edge research in the broad domains of advanced Machine Learning and Information Security. It will cover feature, perspective and review articles, alongside original research, focusing on the advanced theories and practical applications in mathematics, computer science, electronic information, industrial engineering, control science, communication engineering, and other fields. It is anticipated to serve as the hub for sharing knowledge among researchers. It is published quarterly online by Scilight Press.

Scope

The Journal of Machine Learning and Information Security (JMLIS) is a peer reviewed journal dedicated to advancing cutting-edge research at the intersection of machine learning and information security. We welcome high-quality original research papers and comprehensive review articles that contribute to theoretical advancements, novel methodologies, and real-world applications in these fields. Our journal serves as a platform for researchers and practitioners to explore innovative solutions that enhance the security, robustness, and efficiency of machine learning models, as well as the application of AI driven techniques in cybersecurity. We accept a wide range of academic manuscripts, including original research articles, reviews, and communications, covering topics such as, but not limited to:

• Machine Learning for Security: Adversarial machine learning; Secure federated learning; Privacy-preserving AI models; Trust and fairness in AI.
• Security for Machine Learning: Robust model training; Secure data sharing and encryption for ML; AI-driven intrusion detection and threat mitigation.
• Privacy and Data Protection: Cloud security; Blockchain for data privacy; Differential privacy techniques; Access control and authentication.
• Image and Signal Security: Secure image encryption and transmission; AI-based blind image denoising; Image quality evaluation for forensic analysis.
• Intelligent Systems and Network Security: Reinforcement learning for secure intelligent systems; Cooperative control in cybersecurity; Networked system optimization with AI.

By fostering interdisciplinary collaboration between machine learning and information security, this journal aims to bridge the gap between theoretical research and practical implementation. We invite researchers, engineers, and industry experts to contribute pioneering work that will shape the future of secure and intelligent computing.