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Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean

The security of a network requires the correct identification and characterization of the attacks through its ports. This involves the follow-up of all the requests for access to the networks by all kinds of users. We consider the frequency of connections and the type of connections to a network, an...

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Detalles Bibliográficos
Autores principales: Gzyl, Henryk, ter Horst, Enrique, Peña-Garcia, Nathalie, Torres, Andres
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670514/
https://www.ncbi.nlm.nih.gov/pubmed/37998168
http://dx.doi.org/10.3390/e25111476
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author Gzyl, Henryk
ter Horst, Enrique
Peña-Garcia, Nathalie
Torres, Andres
author_facet Gzyl, Henryk
ter Horst, Enrique
Peña-Garcia, Nathalie
Torres, Andres
author_sort Gzyl, Henryk
collection PubMed
description The security of a network requires the correct identification and characterization of the attacks through its ports. This involves the follow-up of all the requests for access to the networks by all kinds of users. We consider the frequency of connections and the type of connections to a network, and determine their joint probability. This leads to the problem of determining a joint probability distribution from the knowledge of its marginals in the presence of errors of measurement. Mathematically, this consists of an ill-posed linear problem with convex constraints, which we solved by the method of maximum entropy in the mean. This procedure is flexible enough to accommodate errors in the data in a natural way. Also, the procedure is model-free and, hence, it does not require fitting unknown parameters.
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spelling pubmed-106705142023-10-24 Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean Gzyl, Henryk ter Horst, Enrique Peña-Garcia, Nathalie Torres, Andres Entropy (Basel) Article The security of a network requires the correct identification and characterization of the attacks through its ports. This involves the follow-up of all the requests for access to the networks by all kinds of users. We consider the frequency of connections and the type of connections to a network, and determine their joint probability. This leads to the problem of determining a joint probability distribution from the knowledge of its marginals in the presence of errors of measurement. Mathematically, this consists of an ill-posed linear problem with convex constraints, which we solved by the method of maximum entropy in the mean. This procedure is flexible enough to accommodate errors in the data in a natural way. Also, the procedure is model-free and, hence, it does not require fitting unknown parameters. MDPI 2023-10-24 /pmc/articles/PMC10670514/ /pubmed/37998168 http://dx.doi.org/10.3390/e25111476 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gzyl, Henryk
ter Horst, Enrique
Peña-Garcia, Nathalie
Torres, Andres
Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title_full Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title_fullStr Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title_full_unstemmed Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title_short Understanding the Feature Space and Decision Boundaries of Commercial WAFs Using Maximum Entropy in the Mean
title_sort understanding the feature space and decision boundaries of commercial wafs using maximum entropy in the mean
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670514/
https://www.ncbi.nlm.nih.gov/pubmed/37998168
http://dx.doi.org/10.3390/e25111476
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