Study on Machine Learning Techniques for Botnet Detection
Abstract
This paper presents a study on the application of machine learning techniques for botnet detection, compromised computer networks controlled by an attacker in order to perform malicious activities, such as distributed denial-of-service attacks (DDoS), data theft and others. The study aims to evaluate the efficiency of commonly used classifiers in the literature for botnet traffic classification and, to this end, we compare the results obtained from each classifier using two different approaches for feature selection, the first one taking into account the most frequently used features in problems of this nature, based on previous works, and the second one taking into account features selected by the Recursive Feature Elimination algorithm, a relatively unexplored feature selection method in the botnet detection area.
How to cite this document
Language
Collections

Related items
Showing items related by title, author, creator and subject.
-
Núcleos de Ensino da Unesp: artigos 2009
Pinho, Sheila Zambello de; Oliveira, José Brás Barreto de
; Gazola, Rodrigo José Cristiano
; Mazotti, Adriano César
; Molero, Camila Schimite
; Mendes, Carolina Borghi
; Mello, Denise Fernandes de
; Marques, Emilia de Mendonça Rosa
; Talamoni, Jandira Liria Biscalquini
; Silva, José Humberto Dias da
et al. (Coleção PROGRAD (UNESP), 2011) [Livro]
-
Núcleos de Ensino da Unesp: artigos 2008
Pinho, Sheila Zambello de; Oliveira, José Brás Barreto de
; Pontes, Sueli Rodrigues
; Almeida, Djanira Soares de Oliveira e
; Godoy, Kathya Maria Ayres de
; Rosa, Claudia de Souza
; Nunes, Julianus Araújo
; Salvador, Sérgio Azevedo
; David, Célia Maria
; Vilche Peña, Angel Fidel
et al. (Coleção PROGRAD (UNESP), 2011) [Livro]
-
Ser e tornar-se professor: práticas educativas no contexto escolar
Pinho, Sheila Zambello de; Spazziani, Maria de Lourdes
; Mendonça, Sueli Guadelupe de Lima
; Rubo, Elisabete Aparecida Andrello
; Villarreal, Dalva Maria de Oliveira
; Duarte, Camila
; Okamoto, Mary Yoko
; Souza, Thais R.
; Garms, Gilza Maria Zauhy
; Marin, Fátima Aparecida Dias Gomes
et al. (Coleção PROGRAD (UNESP), 2012) [Livro]