A Feature Selection Study on the Bot-IoT Dataset Using Ensemble Classification Techniques

Abstract

IoT is an emerging giant in the field of technol- ogy, taking over traditional systems, providing interconnected- ness, convenience, efficiency, and automation, making our lives unimaginably better. However, security for these IoT systems is challenging, especially due to their interconnectedness, making them vulnerable to various cyber threats. The rising tide of IoT botnets, especially, presents a unique challenge. This has urgently increased the need for Intrusion Detection research. Modern Intrusion Detection approaches often employ Machine Learning for effective results. Feature Selection is extremely important while creating Machine Learning Classification models to avoid overfitting and poor performance. This paper focuses on running a Feature Selection study on the Bot-IoT dataset provided by UNSW to increase the accuracy of a ML model. The paper tests 5 types of Feature Selection methods, from Filter- based, Wrapper-based and Embedded methods, combined with two distinct ensemble classifiers: Random Forest + Adaboost and XGBoost. Each combination is tested with the dataset, and the accuracy is compared to find the most effective and versatile feature selection method that can assist both Stacking and Voting- type Ensemble classifiers. The results show that Karl Pearson can provide the best accuracy when applied to both Ensemble Classifiers.