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oai:riuma.uma.es:10630/57262026-02-03T11:22:10Zcom_10630_2254col_10630_37953

De la Hoz Franco, Emiro Ortiz-García, Andrés Ortega, Julio De la Hoz Correa, Eduardo 2013-09-17T08:43:19Z 2013-09-17T08:43:19Z 2013-09 http://hdl.handle.net/10630/5726 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. eng open access Redes de ordenadores - Medidas de seguridad Network Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniques journal article