RF-DNA: Large-Scale Physical-layer Identifications of RFIDs via Dual Natural Attributes

Abstract

Physical-layer identification aims to identify wireless devices during RF communication by exploiting the imperfections of their radio circuitry, i.e., hardware fingerprint. Previous work proposed several hardware fingerprints for RFIDs (e.g., TIE, ABD, PSD, etc). However, these proposed fingerprints suffer from either unscalability or acquisition inefficiency. This work presents RF-DNA, a new hardware fingerprint composed of millions of Dual Natural Attributes (DNA) organized in a helical structure, where a pair of DNA represents a tag's intrinsic response at some frequency. We take advantage of the frequency agnostic phenomenon that a commercial RFID tag can respond within a wider band than the regulated, to acquire 10X more features than previous fingerprints. At the heart of this work are the context-free acquisition approach to extracting DNA from backscatter signals; and the accurate DNA matching algorithm for verifying a tag's identity. A total of 160,000 RF-DNA instances were collected from 16,000 tags using a customized automatic acquisition system. We subsequently carried out large-scale experiments to test the identification accuracy of RF-DNA and previously proposed fingerprints. Our comprehensive evaluation reveals that RF-DNA can achieve a mean accuracy of 95.98%. In contrast, those of previous fingerprints fall to 60% below when in face of thousands of tags.