Machine Learning for Anomaly Detection
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 6
Special Issue Editor
Special Issue Information
Dear Colleagues,
As digital systems grow increasingly complex and interconnected, generating vast volumes of high-dimensional data streams, the ability to identify unusual or malicious activities becomes paramount for security, reliability, and operational integrity. Traditional anomaly detection methods, often reliant on predefined rules or static thresholds, struggle to keep pace with the dynamic nature of modern threats and the sheer scale and complexity of contemporary data environments. These limitations expose critical infrastructure, financial systems, healthcare networks, and industrial processes to significant, evolving risks. In this context, Machine Learning (ML) emerges as a transformative paradigm for anomaly detection, offering the potential to learn intricate patterns from data, adapt to new behaviors, and uncover subtle, previously unknown threats with unprecedented accuracy and efficiency.
This Special Issue invites high-quality, original research papers that explore how machine learning (ML) techniques—such as deep learning, graph neural networks, ensemble methods, and self-supervised learning—can be leveraged to advance anomaly detection in complex, high-dimensional systems.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following: industrial systems anomaly detection, critical infrastructure anomaly detection, image & video anomaly detection, high-dimensional complex data processing and analysis, multimodal complex data processing and analysis, etc.
I look forward to receiving your contributions.
Dr. Da Yang
Guest Editor
Manuscript Submission Information
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Keywords
- anomaly detection
- industrial systems
- critical infrastructure
- image & video
- high-dimensional
- multimodal
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