Securing Emerging IoT Environments with Super Learner Ensembles
Abdelraouf M. Ishtaiwi, Ali Al Maqousi, Amjad Yousef Aldweesh · 2024
This paper investigates the efficacy of the Super Learner ensemble algorithm for robust anomaly detection in Internet of Things (IoT) network traffic. The recently released CIC IoT Dataset 2023, which contains both normal background traffic and common cyber attack patterns, is utilized as an evaluation benchmark. The Super Learner ensemble integrates five diverse base classifiers - Support Vector Machines (SVMs), logistic regression, neural networks, random forests, and K-Nearest Neighbors (KNNs). Extensive empirical analysis is conducted by training on a large stratified sample and testing generalization performance on a held-out set. Results demonstrate that the heterogeneous Super Learner ensemble achieves 94.2% test accuracy in detecting anomalies, which significantly outperforms any individual base model by over 7 percentage points. Precision, recall, and F1 metrics are also markedly improved by the ensemble approach compared to single-learner solutions.