Authors:
Stephanie M. Opoku
1
;
Habib Louafi
2
and
Malek Mouhoub
1
Affiliations:
1
Department of Computer Science, University of Regina, Regina, Canada
;
2
Department of Science of Technology, TELUQ University, Montreal, Canada
Keyword(s):
IoT, Device Identification, Fingerprinting, Network Traffic, Feature Extraction, Maximum Mean Discrepancy, Transfer Learning, Machine Learning.
Abstract:
The rapid growth and diversity of Internet of Things (IoT) devices pose significant challenges in device identification and network security. Traditional techniques for fingerprinting IoT devices frequently encounter challenges due to the complexity and scale of today’s IoT networks. This paper presents an innovative model applying transfer learning (TL) methodologies to analyze network data and precisely identify IoT devices. Our solution effectively classifies devices by integrating instance-based, feature-based, and hybrid-based TL methodologies for extracting essential features from traffic data. The proposed model undergoes a thorough evaluation on three benchmark IoT datasets, exhibiting improved prediction accuracy, precision, recall, and F1-Score relative to traditional methods. The hybrid technique significantly improves performance by handling computational and scalability issues. This paper highlights the effectiveness of TL in improving IoT device identification, providin
g an efficient and effective solution for various and dynamic network environments.
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