RT Journal Article
T1 PCA filtering and Probabilistic SOM for Network Intrusion Detection.
A1 De la Hoz Correa, Eduardo
A1 De la Hoz Franco, Emiro
A1 Ortiz-García, Andrés
A1 Ortega, Julio
A1 Prieto, Beatriz
K1 Estadística bayesiana
K1 Redes neuronales (Informática)
K1 Análisis en componentes principales
K1 Seguridad informática
AB The growth of the Internet and, consequently, the number of interconnected computers, has exposed significant amounts of information to intruders and attackers. Firewalls aim to detect violations according to a predefined rule-set and usually block potentially dangerous incoming traffic. However, with the evolution of attack techniques, it is more difficult to distinguish anomalies from normal traffic. Different detection approaches have been proposed, including the use of machine learning techniques based on neural models such as Self-Organizing Maps (SOMs). In this paper, we present a classification approach that hybridizes statistical techniques and SOM for network anomaly detection. Thus, while Principal Component Analysis (PCA) and Fisher Discriminant Ratio (FDR) have been considered for feature selection and noise removal, Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections. The detection capabilities of the proposed system can be modified without retraining the map, but only by modifying the units activation probabilities. This deals with fast implementations of Intrusion Detection Systems (IDS) necessary to cope with current link bandwidths.
PB Elsevier
YR 2015
FD 2015-09-21
LK https://hdl.handle.net/10630/28118
UL https://hdl.handle.net/10630/28118
LA eng
NO Ortiz, Andrés & Hoz, Eduardo & De la Hoz, Emiro & Ortega, Julio & Prieto, Beatriz. (2014). PCA filtering and Probabilistic SOM for Network Intrusion Detection. Neurocomputing.
NO This work has been funded by the Ministerio de Ciencia e Innovación of the Spanish Government and FEDER Funds under Project no. TIN2012-32039. The authors would like to thank the reviewers for their useful comments and suggestions.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026