Comprehensive Study of KDD99 Dataset and Data Mining Tools for Intrusion Detection

Abstract

Due to extensive growth of the Internet and increasing availability of tools and methods for intruding and attacking networks, intrusion detection has become a critical component of network security parameters. Intrusion detection in large data is one of the major challenges for the researchers in this area. Anomaly detection using data mining techniques has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. In this paper, we have conducted a comprehensive study and statistical analysis on KOO dataset. The authors also provide description of features and instances of the dataset. The another important challenge for the researchers in this area is to select an appropriate data mining tool for the analysts. The paper disusses two important and popular tools in this area, weka, Oracle data mining and tanagara. The authors hope that the study carried out in this paper is useful for the reasearcheres In the area of Intrusion detection.