Domain adaptation based machine learning transferability model
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Date
2024-06
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University of New Brunswick
Abstract
In this study, we tackle the pressing issue of accurately identifying IoT devices across a variety of operational environments. With the ever-changing and dynamic nature of IoT ecosystems at the forefront, we introduce an innovative approach for device identification. Our solution hinges on the power of machine learning models’ transferability, enhanced by domain adaptation techniques. These techniques are key in addressing the inconsistencies present across different IoT settings. Our method involves a detailed examination of network packets to extract essential features, which are then utilized in machine learning algorithms to ensure precise device identification despite potential domain shifts and class imbalances. Leveraging this approach, we employ datasets such as IMC’19, CICIoT 2022, and Sentinel to test and validate our module. The outcome is noteworthy; we achieve a significant 98% accuracy in both testing and evaluation phases, demonstrating the effectiveness of our method in the complex landscape of IoT device identification.