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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10670514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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