Machine Learning and Its Application for Anomaly Detection
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 44
Special Issue Editors
Interests: predictive maintenance; data analysis; image processing; autonomous UAVs; generative AI; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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
- explainable AI (XAI)
- time-series analysis
- generative AI
- predictive maintenance
- cybersecurity
- industrial monitoring
- healthcare analytics
- real-time systems
- federated learning
- privacy-preserving machine learning
- image anomaly detection
- edge computing
- cloud-based detection
- outlier detection
- adaptive detection systems and industrial IoTs
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