Feature Selection Methods for Intrusion Detection Systems in IoT
Ons Saadallah, Ikrame Nouar · 2025
IoT systems enhance daily life through smart applications but are vulnerable due to the increasing number of con-nected devices. To address security concerns, Intrusion Detection Systems (IDS) are crucial for detecting and preventing attacks. While various IDS techniques use computational datasets, many features are irrelevant, prompting the use of feature selection (FS) methods, such as wrapper and embedded techniques, to improve performance. This paper evaluates the impact of FS methods on machine learning models like Naive Bayes, Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors, and SVM for binary and multi-class classification of network attacks. It also explores FS effects on a CNN-LSTM model using the BoTNeTIoT dataset.