Building an Intrusion Detection System Using a Filter Based Fast Co-Relation Algorithm

Abstract

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a filter based fast Correlation-based feature selection (cfs): cfs searches feature subsets according to the degree of redundancy among the features. The evaluator aims to find the subsets of features that are individually highly correlated with the class but have low inter-correlation. The subset evaluators use a numeric measure, such as conditional entropy, to guide the search iteratively and add features that have the highest correlation with the class...