Intrusion Detection System using Fuzzy Rough
Set Feature Selection and Modified KNN Classifier
Balakrishnan Senthilnayaki1, Krishnan Venkatalakshmi2,
and Arpputharaj Kannan1
1Department
of Information Science and Technology, College of Engineering, Anna University,
Chennai
2Departemnt
of Electronics and Communication Engineering, University College of Engineering
Tindivanam, Anna University, Tindivanam
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.
Keywords: Rough set, fuzzy set,
feature selection, classifications and intrusion detection.
Received June 9, 2015; accepted March 9, 2016