IoT Device Identification Using a Meta-Ensemble Multi-Class Classifier
Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris B. Kotsiantis, Achilles D. Kameas · 2023
Device identification in the Internet of Things, is a hot research topic nowadays, offering advantages that enable the widespread adoption of IoT systems. Device identification can be done through various methods such as hardware IDs, finger-printing, and technical features. Machine Learning techniques through big data analysis offer not only an alternative but also an efficient way for device detection and identification. This paper applies a wide set of existing machine learning classifiers for IoT device identification, using a public dataset of IoT devices for multi-class classification. Evaluation results show that a meta-enseble classifier that utilizes the soft voting technique of the three top performing classifiers outperforms all the other models that were employed in our research.