Special Issue "Information Theory Approaches in Anomaly Detection"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (20 December 2019).

Special Issue Editor

Prof. Dr. Alberto Muñoz
Website
Guest Editor
Department of Statistics, University Carlos III de Madrid (UC3M), Madrid, Spain
Interests: semiparametric techniques ( neural network, SVM) for pattern's recognition problems; visualisation and textual information process; cluster analysis and classification; simulation methods and computing statistics; information theory approaches

Special Issue Information

Dear Colleagues,

Anomaly detection (also known as outlier detection) is a problem that arises in every discipline related to data analysis and consists of the identification of uncommon and usually scarce observations that deviate from the bulk of data. This is a difficult problem to solve, given the relativeness of the concepts involved in the definition of outliyingness.
The notion of statistical distribution being an essential element of this problem, it is to be expected that concepts of information theory such as the entropy of a distribution will play a central role in the development of practical methods for anomaly detection. The knowledge of the statistical distribution of the data would be enough to identify the outlying instances. However, the estimation of a data distribution from a sample is one of the most challenging problems in Statistics, a problem that is aggravated as the dimensionality of data increases. In this sense, the aim of this Special Issue is to explore the use of tools/proposals of Information Theory, which is capable of being used to solve the problem of anomaly detection from new perspectives.
Topics of interest include but are not limited to theoretical approaches and applications of Information Theory for anomaly detection in the following areas:

  • Network intrusion;
  • Fraud detection;
  • Healthcare systems;
  • Industrial applications;
  • Image processing;
  • Novel topic detection in text mining;
  • Supervised and unsupervised methods;
  • Independent component analysis;
  • Maximum entropy methods;
  • Time series analysis;
  • Bio-inspired approaches;
  • Neural networks.

Prof. Dr. Alberto Muñoz
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. Entropy is an international peer-reviewed open access monthly 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 1600 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.

Published Papers

There is no accepted submissions to this special issue at this moment.
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