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: 20 December 2026 | Viewed by 995
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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-anonymized 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
- anomaly detection
- industrial systems
- critical infrastructure
- image & video
- high-dimensional
- multimodal
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
