Enhancing IoT Security using Feature Reduction Techniques

Abstract: As Internet of Things (IoT) devices become more common, they’re also becoming a biggertarget for cyberattacks. Thesedevicesoften have limited resources, making it tough to keep them secure. One promising solution is using machine learning in Network Intrusion Detection Systems (NIDS). However, for these systems to work efficiently, they need to reduce the number of features they analyze—this helps save computational power without sacrificing accuracy. In this study, we take a close look at different ways to reduce features, comparing techniques like feature selection (FS)—such as Pearson Correlation and Chi-square—with feature extraction (FE) methods like Principal Component Analysis (PCA) and Autoencoders (AE). We tested these approaches using two IoT- specific datasets, TON-IoT and BoT-IoT, and evaluated five machine learning models: Decision Tree,RandomForest,k-NearestNeighbors,Naive Bayes, and Multi-Layer Perceptron. The results show that FE methods tend to perform better in terms of accuracy and robustness, especially with complex datasets, though they require more computational power. On the other hand, FS techniques like Chi-square offer a good balance between performance and efficiency. Among FE methods,PCAisfasterthanAE.Interestingly,FS works better with smaller datasets, while FE is more effective for handling a variety of attack types.Thisresearchprovidespracticaladviceon choosing the right feature reduction method for IoT environments, helping strike a balance between accuracy and computational efficiency. These insights are crucial for building scalable, real-time NIDS that can handle the unique challenges of IoT systems, benefiting both researchers and industry professionals working on IoT security.