Abstract
An Intrusion detection system is a key component of the security management infrastructure. Machine learning advances has benefited many domains including the security domain. Anomaly based Intrusion Detection Systems using machine learning techniques can be trained to detect even unknown attacks. In this paper we conduct a comprehensive review of various researches related to Machine Learning based IDS using the NSL-KDD data set. We propose a generic process flow for anomaly-based IDS and describe this process flow components in the context of related researches carried out. We then point out interesting future research ideas.
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