Machine learning techniques applied to intruder detection in networks

Author

Henao R, J.L. ; Espinosa O, J.E.

Author_Institution

Politec. Colombiano “Jaime Isaza Cadavid”, Medellin, Colombia

fYear

2013

Firstpage

1

Lastpage

6

Abstract

The intrusion in data networks, are a constant problem faced by networks administrators. Because of this, it is necessary identify, study and propose techniques to detect the moment in which the network is attacked, with the purpose of take measures to mitigate these threats. In this paper was conducted a study of the threats taxonomy that could lead to an attack in a data network. For this, we have identified the most relevant characteristics of the network traffic in order to be processed and classified using machine learning techniques, specifically the normalization (Z-Score), dimensionality reduction (PCA) and classification based on artificial neural networks (ANN) to suggest an intrusion detection system (IDS).

Keywords

learning (artificial intelligence); neural nets; pattern classification; principal component analysis; security of data; ANN classification; IDS; PCA; Z-score; artificial neural networks; data networks; dimensionality reduction; intruder detection; intrusion detection system; machine learning techniques; network traffic; normalization; principal component analysis; threat mitigation; threats taxonomy; Artificial neural networks; Boolean functions; Data structures; Vectors; IDS; Intruder Detection Systems; Threat; classification; dimensionality reduction; network attacks; neural networks; normalization;

fLanguage

English

Publisher

ieee

Conference_Titel

Security Technology (ICCST), 2013 47th International Carnahan Conference on

Conference_Location

Medellin

Type

conf

DOI

10.1109/CCST.2013.6922081

Filename

6922081