Effects-based feature identification for network intrusion detection
Neurocomputing(2013)
Abstract
Intrusion detection systems (IDS) are an important element in a network's defences to help protect against increasingly sophisticated cyber attacks. IDS that rely solely on a database of stored known attacks are no longer sufficient for effectively detecting modern day threats. This paper presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering, Naive Bayes feature selection and C4.5 decision tree classification for pinpointing cyber attacks with a high degree of accuracy in order to increase the situational awareness of cyber network operators.
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Key words
high degree,decision tree classification,cyber network operator,sophisticated cyber attack,naive bayes feature selection,network intrusion detection,cyber attack,attack feature,effects-based feature identification,novel anomaly detection technique,effects-based feature identification method,intrusion detection system,decision trees,intrusion detection,classification,clustering,feature selection
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