TY - GEN
T1 - IoT Device Identification Using a Meta-Ensemble Multi-Class Classifier
AU - Davrazos, Gregory
AU - Panagiotakopoulos, Theodor
AU - Kotsiantis, Sotiris
AU - Kameas, Achilles
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Device Identification
KW - Internet of Things
KW - Machine Learning Algorithms
KW - Meta Ensemble
KW - PyCaret
KW - Soft Voting
UR - http://www.scopus.com/inward/record.url?scp=85182021716&partnerID=8YFLogxK
U2 - 10.1109/IISA59645.2023.10345911
DO - 10.1109/IISA59645.2023.10345911
M3 - Conference contribution
AN - SCOPUS:85182021716
T3 - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
BT - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
Y2 - 10 July 2023 through 12 July 2023
ER -