Comparative Study of Feature Selection Methods for Deep Learning–based Intrusion Detection in IoT Networks
Keshav Gogia, Rakshan Narula, Kunal Bansal, Anshul Arora · 2025
As the widespread proliferation of Internet of Things (IoT) networks, securing devices that are networked together is now a serious issue. Intrusion Detection Systems (IDS) based on deep learning have emerged as an intriguing solution due to their ability to learn complex patterns in network traffic. Yet, high-dimensional input data—since they are often redundant or irrelevant features—may hinder detection effectiveness and increase computational costs, particularly on resource-constrained IoT gateways. This paper is an extensive comparison of three distinct feature selection methods—Principal Component Analysis (PCA), Mutual Information–guided Multi-population Differential Evolution (MI-MPODE), and Statistical Moments ranking—implemented in the same 1D Convolutional Neural Network (CNN) architecture and compared on the CIC-IoT-2023 dataset. We provide complete theoretical derivations, mathematical expressions, and implementation details for each method. Experiments with 80/20 train/test split across 687,179 flows with labels for 34 classes of traffic prove that MI-MPODE has the highest detection accuracy (81.27%) with the least feature reduction of over 50%. PCA yields 75.38% with only 15 principal components, and Statistical Moments yield 48.99% with 30 features. We offer extensive per-class performance, computational overhead, and practical considerations for deploying IDS on IoT platforms.