| Abstract |
Nowadays, wireless networks have been widely deployed in our daily lives, providing people with convenient Internet of Things (IoT) services in healthcare, smart cities, transportation, etc. However, the open nature of communication mediums leaves IoT devices susceptible to unauthorized access by rogue devices, leading to significant privacy breaches and property damage. Among various security measures, radio frequency (RF) fingerprinting stands out as a promising device identification technique, owing to RF fingerprints' uniqueness and forgery-resistant nature. Existing methods, however, overlook the structural relationship of a transmitter's internal hardware paths, affecting the performance and efficiency of RF fingerprint identification. Inspired by the internal hardware paths, this article introduces a novel deep-learning-based RF fingerprinting approach, multidimensional RF fingerprinting enhancement (MRFE). MRFE enhances RF fingerprinting by dissecting raw IQ signals into multiple dimensions and proposing a novel fingerprint strengthen layer (FSL) to extract multidimensional fingerprints from the separate hardware paths, then leveraging attention mechanisms to fuse them into an enhanced RF fingerprint. The enhanced fingerprint captures more detailed physical hardware characteristics, effectively enhancing device identification accuracy. Our MRFE's open-source implementation has been validated on the public ORACLE RF fingerprinting data set, achieving an impressive 99.33% accuracy in identifying 16 high-end bit-similar transmitters with identical configurations. © 2014 IEEE. |
