Balanced Datasets for IoT IDS
As the Internet of Things (IoT) continues to grow, cyberattacks are becoming
increasingly common. The security of IoT networks relies heavily on intrusion
detection systems (IDSs). The development of an IDS that is accurate and
efficient is a challenging task. As a result, this challenge is made more
challenging by the absence of balanced datasets for training and testing the
proposed IDS. In this study, four commonly used datasets are visualized and
analyzed visually. Moreover, it proposes a sampling algorithm that generates a
sample that represents the original dataset. In addition, it proposes an
algorithm to generate a balanced dataset. Researchers can use this paper as a
starting point when investigating cybersecurity and machine learning. The
proposed sampling algorithms showed reliability in generating well-representing
and balanced samples from NSL-KDD, UNSW-NB15, BotNetIoT-01, and BoTIoT
datasets.