This chapter presents a comprehensive evaluation of five feature selection methods (SelectKBest, Recursive Feature Elimination (RFE), Random Forest, L1-Based Selection (Lasso), and Mutual Information) in the context of Intrusion Detection for Internet of Things (IoT) systems. We use the CIC IoT 2023 dataset, a realistic and extensive dataset for large-scale attacks in IoT environments. Our overall evaluation is based on traditional criteria such as performance (Accuracy, Precision, Recall, and F1-Score), computational efficiency (training time, response time, and memory usage), noise immunity (Jaccard index), and Redundancy Score (Pearson coefficient). The experimental results provide insights into the advantages and disadvantages of each method, thereby providing suggestions for choosing the best feature selection technique based on the particular needs of the machine learning-based intrusion detection system for the Internet of Things environment.