Machine Learning and Its Application for Anomaly Detection

Dear Colleagues,

Anomaly detection plays a critical role in various domains, including cybersecurity, finance, industrial monitoring, and healthcare. Traditional detection methods often face challenges when dealing with high-dimensional data, rapidly changing environments, and intricate anomaly patterns. In contrast, machine learning (ML) has emerged as a robust solution, equipping practitioners with sophisticated techniques that enhance accuracy, scalability, and adaptability in anomaly detection.

This Special Issue seeks to showcase pioneering research and innovative applications of ML in the field of anomaly detection. We invite submissions that advance both theoretical frameworks and practical implementations, addressing topics such as, but not limited to, deep learning-based anomaly detection, real-time monitoring systems, the interpretability of ML-driven anomaly detection, and the synergistic integration of ML with edge and cloud computing.

Potential topics of interest include, but are not limited to:

• Supervised, unsupervised, and semi-supervised ML methodologies for anomaly detection;
• The utilization of deep learning and generative models for anomaly identification;
• Anomaly detection applications in cybersecurity, finance, healthcare, and industrial systems;
• Time-series anomaly detection and predictive maintenance strategies;
• Federated and privacy-preserving ML techniques focused on anomaly detection;
• Image anomaly detection (medical imaging, industrial inspection, remote sensing, and security);
• Frameworks for real-time and streaming anomaly detection;
• Explainable and interpretable ML approaches in the context of anomaly detection.

We encourage researchers and practitioners to submit original research articles, case studies, and reviews that contribute to the evolution of ML-based anomaly detection. This Special Issue aims to foster interdisciplinary dialogue and present novel solutions that advance the frontiers of anomaly detection technologies.

Dr. Naeem Ayoub
Dr. Amira Mouakher
Guest Editors

Manuscript Submission Information

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