Intrusion detection systems (IDS) for the Internet of Things (IoT) systems
can use AI-based models to ensure secure communications. IoT systems tend to
have many connected devices producing massive amounts of data with high
dimensionality, which requires complex models. Complex models have notorious
problems such as overfitting, low interpretability, and high computational
complexity. Adding model complexity penalty (i.e., regularization) can ease
overfitting, but it barely helps interpretability and computational efficiency.
Feature engineering can solve these issues; hence, it has become critical for
IDS in large-scale IoT systems to reduce the size and dimensionality of data,
resulting in less complex models with excellent performance, smaller data
storage, and fast detection. This paper proposes a new feature engineering
method called LEMDA (Light feature Engineering based on the Mean Decrease in
Accuracy). LEMDA applies exponential decay and an optional sensitivity factor
to select and create the most informative features. The proposed method has
been evaluated and compared to other feature engineering methods using three
IoT datasets and four AI/ML models. The results show that LEMDA improves the F1
score performance of all the IDS models by an average of 34% and reduces the
average training and detection times in most cases.

提出了一种名为 LEMDA 的新型特征工程方法，通过选择和创建最有信息的特征，减少大规模物联网系统中的数据大小和维度，使得模型更简单且具有优异的性能、更小的数据存储以及更快的检测速度。