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Supervised learning algorithm for Intrusion Detection needs labeled data for training. Lots of data is available through internet, network and host. But this data is unlabeled data. The availability of labeled data needs human expertise which is costly. This is the main hurdle for developing supervised intrusion detection systems. The authors can intelligently use both labeled and unlabeled data for intrusion detection. Semi-supervised learning has attracted the attention of the researcher working in Intrusion Detection using machine learning. Their goal is to improve the classification accuracy of any given supervised classifier algorithm by using the limited labeled data and large unlabeled data.
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