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.

评估了使用不同的特征选择方法（信息增益、卡方检验、递归特征消除、平均绝对偏差和离散比）提供的特征集在多个 IoT 网络数据集中的影响，以提高计算机效率。结果显示在保持良好泛化能力的同时，提高了机器学习模型的计算效率，对于每个数据集识别出了最具影响力的特征，并且特征集之间几乎没有差异。