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. We 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. Our goal is to improve the classification accuracy of any given supervised classifier algorithm by using the limited labeled data and large unlabeled data. The key advantage of the proposed semi-supervised learning approach is to improve the performance of supervised classifier. The results show that the performance of the proposed semi-supervised algorithm is better than the state-of the- art supervised learning algorithms. We compare the performance of our DS-AdaBoost algorithm as well as 5 standard algorithms available in WEKA for supervised and semi-supervised approach.

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Intrusion Detection supervised learning semi-supervised learning pattern recognition