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

Author:

Gzyl Henryk1ORCID,ter Horst Enrique2ORCID,Peña-Garcia Nathalie3ORCID,Torres Andres2ORCID

Affiliation:

1. Centro de Finanzas IESA, Caracas 1010, Venezuela

2. School of Management, Universidad de los Andes, Bogota 111711, Colombia

3. Research Department, CESA Business School, Bogota 110311, Colombia

Abstract

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.

Publisher

MDPI AG

Subject

General Physics and Astronomy

Reference13 articles.

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3. Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F. (2013). Advanced Information Systems Engineering, Springer. Lecture Notes in Computer Science.

4. Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., and Swami, A. (2017, January 2–6). Practical Black-Box Attacks against Machine Learning. Proceedings of the ACM Asia Conference on Computer and Communications Security, Abu Dhabi, United Arab Emirates.

5. Network intrusion detection system: A systematic study of machine learning and deep learning approaches;Ahmad;Trans. Emerg. Telecommun. Technol.,2020

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