Network Intrusion Detection Demystified Using Classification Trees

Abstract

In a network environment, intrusions pose a serious security problem especially when new intrusion types are discovered which make them difficult to detect. In this work, we use the Classification And Regression Tree (CART) algorithm, a supervised learning technique, on a labeled dataset collected during an attack on the Faculty of Science?s web servers, and this in order to classify bad and good connections. As result, the accuracy of the classification reached 99%, maintaining low false-negative and low false-positive rates.