  With the rapidly spreading usage of Internet of Things (IoT) devices, a
network intrusion detection system (NIDS) plays an important role in detecting
and protecting various types of attacks in the IoT network. To evaluate the
robustness of the NIDS in the IoT network, the existing work proposed a
realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to
machine learning-based anomaly detection. This dataset contains imbalanced
normal and attack packets because the number of normal packets is much smaller
than that of attack ones. The nature of imbalanced data may make it difficult
to identify the minority class correctly. In this thesis, to address the class
imbalance problem in the Bot-IoT dataset, we propose a binary classification
method with synthetic minority over-sampling techniques (SMOTE). The proposed
classifier aims to detect attack packets and overcome the class imbalance
problem using the SMOTE algorithm. Through numerical results, we demonstrate
the proposed classifier's fundamental characteristics and the impact of
imbalanced data on its performance.
