Location-Invariant Physical Layer Identification Approach for WiFi Devices

Recently, Radio Frequency Fingerprinting (RFF) becomes a promising technique which augments existing multifactor authentication schemes at the device level to counter forgery and related threats. As RFF leverages the discriminable hardware imperfections reflected in Radio Frequency (RF) signals for device identification, it has a good property of scalability, accuracy, energy-efficiency and tamper resistance. However, its identification accuracy might be compromised when the locations of training and testing are different, which is a more realistic assumption in practical scenarios. To address this issue, we study the location-invariant RFF feature extraction and identification method for WiFi Network Interface Cards (NICs). Firstly, we present an RFF feature extraction approach named Differential Phase of Pilots (DPoP). To further address the low-dimensional feature space problem, we propose another novel RFF extraction approach named Amplitude of Quotient (AoQ). AoQ exploits the fact that the RFFs of two Long Training Sequences (LTSs) in WiFi frames exhibit semi-steady characteristics and two LTSs in the same frame have similar channel frequency responses. Next, we use Euclidean distance and Deep Neural Network (DNN) for AoQ authentication and identification, respectively. Experimental results verify the effectiveness of our proposed AoQ method among 55 WiFi NICs of 5 models. The identification accuracy is higher than 95% and the Equal Error Rate (EER) is around 4% when SNR is higher than 40 dB.