RT Journal Article
T1 Network Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniques
A1 De la Hoz Franco, Emiro
A1 Ortiz-García, Andrés
A1 Ortega, Julio
A1 De la Hoz Correa, Eduardo
K1 Redes de ordenadores - Medidas de seguridad
AB Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification rates.
PB Springer
YR 2013
FD 2013-09
LK http://hdl.handle.net/10630/5726
UL http://hdl.handle.net/10630/5726
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026