Abstract Intrusion detection systems are used to detect and prevent the attacks in networks and databases. However, the increase in the dimension of the network dataset has become a major problem nowadays. Feature selection is used to reduce the dimension of the attributes present in those huge data sets. Classical Feature selection algorithms are based on Rough set theory, neighborhood rough set theory and fuzzy sets. Rough Set Attribute Reduction Algorithm is one of the major theories used for successfully reducing the attributes by removing redundancies. In this algorithm, significant features are selected data are extracted. In this paper, a new feature selection algorithm is proposed using the Maximum dependence Maximum Significance algorithm. This algorithm is used for selecting the minimal number of attributes of knowledge Discovery and Data (KDD) data set. Moreover, a new K-Nearest Neighborhood based algorithm proposed for classifying data set. This proposed feature selection algorithm considerably reduces the unwanted attributes or features and the classification algorithm finds the type of intrusion effectively. The proposed feature selection and classification algorithms are very efficient in detecting attacks and effectively reduce the false alarm rate.

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