Efficient Network Traffic Feature Sets for IoT Intrusion Detection

TL;DR

This paper evaluates various feature selection methods to identify the most impactful features for IoT intrusion detection, improving computational efficiency while maintaining classification performance.

Contribution

It compares multiple feature selection techniques across IoT datasets to determine the most effective features for efficient intrusion detection.

Findings

Selected features enhance ML model efficiency

Small feature sets maintain high classification accuracy

Identified key features for IoT intrusion detection

Abstract

The use of Machine Learning (ML) models in cybersecurity solutions requires high-quality data that is stripped of redundant, missing, and noisy information. By selecting the most relevant features, data integrity and model efficiency can be significantly improved. This work evaluates the feature sets provided by a combination of different feature selection methods, namely Information Gain, Chi-Squared Test, Recursive Feature Elimination, Mean Absolute Deviation, and Dispersion Ratio, in multiple IoT network datasets. The influence of the smaller feature sets on both the classification performance and the training time of ML models is compared, with the aim of increasing the computational efficiency of IoT intrusion detection. Overall, the most impactful features of each dataset were identified, and the ML models obtained higher computational efficiency while preserving a good generalization, showing little to no difference between the sets.