Enhancing Machine Learning Model Prediction with Feature Selection for Botnet Intrusion Detection

Increased vulnerabilities brought about by the explosive growth of the Internet of Things (IoT) call for improved security measures to protect systems from attacks. Intrusion Detection Systems (IDS) that use machine learning (ML) are essential for identifying vulnerabilities. Among various threats, botnets are particularly challenging due to their persistence and complexity. This study explores the application of ML techniques (RF, NB, DT, KNN, LR, and XGBoost) for intrusion detection in IoT networks, with a focus on handling imbalanced data and applying feature selection methods. On the Bot-IoT dataset, the study used Lasso feature selection and the SMOTE data balancing technique to obtain a high accuracy of 99.99% with low execution times using the XGBoost model.