Network anomaly classification by support vector classifiers ensemble and non-linear projection techniques
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De La Hoz, Eduardo | 2013-09-11
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
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