ON THE COMPREHENSIVE ANALYSES OF CTU-13 BOTNET DATASET FOR CYBER SECURITY RESEARCHES

Attackers use malware to launch attacks in the internet and corporate networks. Over the years, machine learning techniques have been found promising for the classification of these attacks because they have the ability to identify unknown threats.  Botnets are networks of compromised devices and have been found to be powerful threat vectors that are used against modern systems because they use command and control (C2) characteristics which make their detection very difficult.  Generally, to build attack detection models, intrusion datasets are employed. Comprehensive study of the benchmarking datasets used in intrusion detection researches can provide different actionable insights to other researchers. There have been studies that investigated the analyses of datasets for building intrusion detection systems. However, there has been less focus on the analysis of intrusion detection datasets that are used specifically for botnets detection. This study reported an overview of a popular botnet dataset named CTU-13. Thereafter, the work carried out detailed exploratory analysis of the dataset. The study equally sought to identify if the dataset is representative enough for Machine Lea