Text mining based approach for intrusion detection

Intrusion detection is classified as NP-Hard in the literature even today. Also supervised learning also termed classification, when performed on high dimensional documents has problem from the noise or outliers, which make the text classification inaccurate and leads to reduced accuracy by classifiers. We discuss the feature reduction methods which we adopted to achieve dimensionality reduction. In the Feature Extraction process, the high dimensional text documents are projected onto their corresponding low dimensional representation in feature space through using algebraic rules and transformations. The objective is to find optimal transformation matrix corresponding the input high dimensional document feature matrix. This objective is achieved in this thesis by using the concept of feature clustering and through clustering the features into a optimal set of clusters by designing a novel fuzzy membership function. The membership function designed retains the original distribution of words in the documents which is the importance of this approach.