A Systematic Hands-On Approach to Generate Real-Life Intrusion Datasets

Abstract

To evaluate a network anomaly detection or prevention, it is essential to test using benchmark network traffic datasets. This chapter aims to provide a systematic hands-on approach to generate real-life intrusion dataset. It is organized in three major sections. Section 3.1 provides the basic concepts. Section 3.2 introduces several benchmark and real-life datasets. Finally, Sect.{\thinspace}3.3 provides a systematic approach toward generation of an unbiased real-life intrusion datasets. We establish the importance of intrusion datasets in the development and validation of a detection mechanism or a system, identify a set of requirements for effective dataset generation, and discuss several attack scenarios.