This paper compares feature selection and feature extraction for intrusion detection in IoT networks. The study uses the TON-IoT dataset and evaluates performance metrics such as accuracy, F1-score, and runtime for binary and multiclass classification. Feature extraction generally provides better detection performance, especially when the number of features is small. It is less sensitive to changes in the number of features and reduces dimensionality more effectively than feature selection. However, feature selection requires less training and inference time and offers more potential for improving accuracy when the number of features changes. Both methods are suitable for IoT networks, but feature extraction is more effective for lightweight and efficient intrusion detection systems. The study provides guidelines for selecting appropriate feature reduction techniques based on the specific requirements of the IoT system. The findings suggest that feature extraction is more suitable for IoT networks due to its efficiency and effectiveness in reducing dimensionality while maintaining critical information. The study also highlights the importance of considering computational complexity and detection accuracy when selecting feature reduction techniques for IoT intrusion detection. The research contributes to the field by providing a comprehensive comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks.This paper compares feature selection and feature extraction for intrusion detection in IoT networks. The study uses the TON-IoT dataset and evaluates performance metrics such as accuracy, F1-score, and runtime for binary and multiclass classification. Feature extraction generally provides better detection performance, especially when the number of features is small. It is less sensitive to changes in the number of features and reduces dimensionality more effectively than feature selection. However, feature selection requires less training and inference time and offers more potential for improving accuracy when the number of features changes. Both methods are suitable for IoT networks, but feature extraction is more effective for lightweight and efficient intrusion detection systems. The study provides guidelines for selecting appropriate feature reduction techniques based on the specific requirements of the IoT system. The findings suggest that feature extraction is more suitable for IoT networks due to its efficiency and effectiveness in reducing dimensionality while maintaining critical information. The study also highlights the importance of considering computational complexity and detection accuracy when selecting feature reduction techniques for IoT intrusion detection. The research contributes to the field by providing a comprehensive comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks.