No Blind Spots: On the Resiliency of Device Fingerprints to Hardware Warm-Up Through Sequential Transfer Learning

Deep Learning-based RF fingerprinting has emerged as a game-changer for offering robust network device authentication and identification solutions. However, it struggles in cross-time scenarios, particularly during hardware warm-up phases. This often-overlooked vulnerability jeopardizes the reliability of these solutions. In response to this critical gap, we dive deep into the anatomy of RF fingerprints, revealing insights into temporal variations in DL-based RF fingerprinting during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our extensive evaluation showcases HEEDFUL's efficacy, achieving remarkable classification accuracies of up to 96% during the initial intervals of device operation-far surpassing traditional models. Cross-domain assessments confirm HEEDFUL's superiority, achieving a steady 87% classification accuracy across warm-up intervals on the Day 2 dataset. Additionally, we release a WiFi RF fingerprinting dataset that, for the first time, incorporates both the time-domain representation and real hardware impairments of the frames. This inclusion underscores the importance of leveraging actual hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more resilient solutions.