Special Issue "Information Theory Approaches in Anomaly Detection"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (20 December 2019).
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
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
Manuscript Submission Information
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