Improving Attack Detection of C4.5 using an Evolutionary Algorithm

Javier Maldonado, Maria Cristina Riff, Elizabeth Montero

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

Intrusion detection is a major research problem in network security. Intrusion Detection Systems (IDS), analyses information from the network trying to identify suspicious behaviors and detect intentions to attack the system. Intrusion attempts are nonlinear with an unpredictable behavior on the network traffic. The process of selecting the key features that allows discriminate attacks from normal traffic, is a crucial task in information security to obtain an effective IDS. We propose in this paper to use an Evolutionary Algorithm and an evaluation function from a classifier, to automatically select key features from a data set before defining a Decision Tree that can be used to discriminate among the network data type. The purpose of this study, is to propose an intrusion detection technique that selects key features using a specially designed evolutionary algorithm with individual evaluations done using C4.5, a wellknown classifier that discriminate data using decision trees. We report very encouraging results of our approach using NSL-KDD intrusion detection benchmark data sets.

Idioma originalInglés
Título de la publicación alojada2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas2229-2235
Número de páginas7
ISBN (versión digital)9781728121536
DOI
EstadoPublicada - 1 jun 2019
Evento2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, Nueva Zelanda
Duración: 10 jun 201913 jun 2019

Serie de la publicación

Nombre2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conferencia

Conferencia2019 IEEE Congress on Evolutionary Computation, CEC 2019
PaísNueva Zelanda
CiudadWellington
Período10/06/1913/06/19

Áreas temáticas de ASJC Scopus

  • Matemática computacional
  • Modelización y simulación

Huella Profundice en los temas de investigación de 'Improving Attack Detection of C4.5 using an Evolutionary Algorithm'. En conjunto forman una huella única.

  • Citar esto

    Maldonado, J., Riff, M. C., & Montero, E. (2019). Improving Attack Detection of C4.5 using an Evolutionary Algorithm. En 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (pp. 2229-2235). [8790199] (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2019.8790199