dc.contributor.author | López Martín, Manuel | |
dc.contributor.author | Sánchez Esguevillas, Antonio Javier | |
dc.contributor.author | Arribas Sánchez, Juan Ignacio | |
dc.contributor.author | Carro Martínez, Belén | |
dc.date.accessioned | 2022-07-25T08:15:39Z | |
dc.date.available | 2022-07-25T08:15:39Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | IEEE Access, 2021, vol. 9, p. 153153-153170 | es |
dc.identifier.issn | 2169-3536 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/54210 | |
dc.description | Producción Científica | es |
dc.description.abstract | Network intrusion detection focuses on classifying network traffic as either normal or attack
carrier. The classification is based on information extracted from the network flow packets. This is a complex
classification problem with unbalanced datasets and noisy data. This work extends the classic radial basis
function (RBF) neural network by including it as a policy network in an offline reinforcement learning
algorithm. With this approach, all parameters of the radial basis functions (along with the network weights)
are learned end-to-end by gradient descent without external optimization. We further explore how additional
dense hidden-layers, and the number of radial basis kernels influence the results. This novel approach is
applied to five prominent intrusion detection datasets (NSL-KDD, UNSW-NB15, AWID, CICIDS2017
and CICDDOS2019) achieving better performance metrics than alternative state-of-the-art models. Each
dataset provides different restrictions and challenges allowing a better validation of results. Analysis of
the results shows that the proposed architectures are excellent candidates for designing classifiers with the
constraints imposed by network intrusion detection. We discuss the importance of dataset imbalance and
how the proposed methods may be critically important for unbalanced datasets. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.classification | Communication system security | es |
dc.subject.classification | intrusion detection | es |
dc.subject.classification | Neural networks | es |
dc.subject.classification | Radial basis function networks | es |
dc.subject.classification | Network intrusion detection | es |
dc.title | Network intrusion detection based on extended RBF neural network with offline reinforcement learning | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2021 IEEE | es |
dc.identifier.doi | 10.1109/ACCESS.2021.3127689 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9612220 | es |
dc.identifier.publicationfirstpage | 153153 | es |
dc.identifier.publicationlastpage | 153170 | es |
dc.identifier.publicationtitle | IEEE Access | es |
dc.identifier.publicationvolume | 9 | es |
dc.peerreviewed | SI | es |
dc.description.project | Ministerio de Ciencia, Innovación y Universidades Proyectos de I+D+i ‘‘Retos investigación’’, (grant RTI2018-098958- B-I00) | es |
dc.identifier.essn | 2169-3536 | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 33 Ciencias Tecnológicas | es |
dc.subject.unesco | 3325 Tecnología de las Telecomunicaciones | es |