Computational Intelligence: Algorithms, Security, and Data-Driven Modeling

Dear Colleagues,

The rapid advancement of artificial intelligence technologies and the widespread adoption of AI-driven services are poised to bring about significant transformations across society. As humanity now enters an era in which it must explore and establish appropriate ways of engaging with artificial intelligence, new and important societal challenges are emerging. At the same time, the progress of AI is shedding new light on a variety of technical fields from fresh perspectives.

In the area of information security, for example, several critical research questions have already become the subject of active debate: How can privacy and sensitive information be protected in AI systems that require vast amounts of training data? Can artificial intelligence itself discover new forms of vulnerabilities? How will the dynamics between cyber offense and defense evolve in an era shaped by AI?

From the viewpoint of algorithms, the current breakthrough in AI—enabled by the discovery of backpropagation—has renewed interest in efficient learning mechanisms. Research on new algorithms that can reduce the immense time and data required for training remains highly active, and AI-driven approaches to algorithm discovery may open entirely new directions in this field.

Furthermore, the remarkable success of machine learning and artificial intelligence has demonstrated the effectiveness of data-driven modeling. The attention mechanism, a core concept of modern large language models, can be interpreted as casting new light on the methodology of data-driven modeling itself.

Artificial intelligence exerts a profound influence on many technological domains; among them, security, algorithms, and modeling are not only essential foundations that will support future advances in AI but also research areas that will be most significantly transformed by AI technologies.

We invite submissions addressing any topics related to these fields in the context of artificial intelligence. Contributions may take the form of problem statements, conceptual or methodological proposals, or concrete technical developments. All high-quality papers that offer new insights or advances are welcome.

Prof. Dr. Kilho Shin
Guest Editor

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• artificial intelligence (AI)
• machine learning algorithms
• data-driven modeling
• cybersecurity and privacy
• adversarial machine learning
• algorithmic efficiency and optimization
• neural networks and deep learning
• attention mechanisms and transformers
• secure and robust learning
• computational intelligence applications
• model interpretability and explainability
• privacy-preserving machine learning