Special Issue "Unsupervised 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: 28 February 2021.

Special Issue Editor

Prof. Dr. Markus Goldstein
Website
Guest Editor
Faculty of Computer Science, Ulm University of Applied Sciences, 89075 Ulm, Germany
Interests: anomaly detection; data science; machine learning; deep learning; NoSQL

Special Issue Information

Anomaly detection (also known as outlier detection) is the task of finding instances in a dataset which deviate from the norm. Anomalies are often of specific interest in many real-world analytic tasks, since they can refer to incidents requiring special attention. Among others, intrusion detection, payment fraud detection, public safety, complex system monitoring, and medical data analytics are possible application domains. Typically, anomaly detection is performed in an unsupervised setting, because no labeled training data are available. This causes many challenges in the research area, including a fair evaluation of algorithms, combing different algorithms (“outlier ensembles”) in a smart way or the interpretability of scores.

Potential topics of interest for this Special Issue include (but are not limited to) the following areas:

  • New or improved unsupervised anomaly detection algorithms;
  • Deep learning for anomaly detection;
  • Interpretability of scores;
  • Outlier ensembles;
  • Unsupervised anomaly detection datasets for benchmarks and quality assessments;
  • Applications of unsupervised anomaly detection, for example, surveillance, intrusion detection, fraud detection, medical applications or monitoring applications;
  • Anomaly detection in time series/ images/ video and text data;
  • Semi-supervised anomaly detection (also known as one-class classification).

Prof. Dr. Markus Goldstein
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 papers will be 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.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 1800 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
  • outlier detection
  • novelty detection
  • outlier ensembles
  • evaluation of unsupervised anomaly detection
  • time series anomaly detection
  • deep learning for anomaly detection
  • unsupervised learning
  • one-class classification

Published Papers

This special issue is now open for submission.
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