An approach for Intrusion Detection using Text Mining Techniques

Abstract

The problem of clustering is NP-Complete. The existing clustering algorithm in literature is the approximate algorithms, which cluster the underlying data differently for different datasets. The K-Means Clustering algorithm is suitable for frequency but not for binary form. When an application runs several system calls are implicitly invoked in the background. Based on these system calls we can predict the normal or abnormal behavior of applications. This can be done by classification. In this paper we tried to perform classification of processes running into normal and abnormal states by using system call behavior. We reduce the system call feature vector by choosing k-means algorithm which uses the proposed measure for dimensionality reduction. We give the design of the proposed measure. The proposed measure has upper and lower bounds which are finite.