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dc.contributor.author
Guerra Torres, Jorge Luis
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dc.contributor.author
Catania, Carlos Adrian
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dc.contributor.author
Veas, Eduardo
dc.date.available
2021-07-12T14:55:31Z
dc.date.issued
2019-12
dc.identifier.citation
Guerra Torres, Jorge Luis; Catania, Carlos Adrian; Veas, Eduardo; Active learning approach to label network traffic datasets; Elsevier; Journal of Information Security and Applications; 49; 12-2019; 1-13
dc.identifier.issn
2214-2126
dc.identifier.uri
http://hdl.handle.net/11336/135852
dc.description.abstract
In the field of network security, the process of labeling a network traffic dataset is specially expensive since expert knowledge is required to perform the annotations. With the aid of visual analytic applications such as RiskID, the effort of labeling network traffic is considerable reduced. However, since the label assignment still requires an expert pondering several factors, the annotation process remains a difficult task. The present article introduces a novel active learning strategy for building a random forest model based on user previously-labeled connections. The resulting model provides to the user an estimation of the probability of the remaining unlabeled connections helping him in the traffic annotation task. The article describes the active learning strategy, the interfaces with the RiskID system, the algorithms used to predict botnet behavior, and a proposed evaluation framework. The evaluation framework includes studies to assess not only the prediction performance of the active learning strategy but also the learning rate and resilience against noise as well as the improvements on other well known labeling strategies. The framework represents a complete methodology for evaluating the performance of any active learning solution. The evaluation results showed proposed approach is a significant improvement over previous labeling strategies.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ACTIVE LEARNING
dc.subject
LABELING NETWORK
dc.subject
LEARNING RATE
dc.subject
NOISE ROBUSTNESS
dc.subject
RANDOM FOREST
dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Active learning approach to label network traffic datasets
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-06-07T15:33:28Z
dc.journal.volume
49
dc.journal.pagination
1-13
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Ámsterdam
dc.description.fil
Fil: Guerra Torres, Jorge Luis. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
dc.description.fil
Fil: Catania, Carlos Adrian. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina
dc.description.fil
Fil: Veas, Eduardo. Graz University Of Technology.; Austria
dc.journal.title
Journal of Information Security and Applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.jisa.2019.102388
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S2214212618304344
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