Location-Invariant Physical Layer Identification Approach for WiFi Devices

Abstract

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. INDEX TERMS Wireless communications, physical layer security, radio frequency fingerprint, device identification, 802.11n OFDM.

Stay Connected, Leave no Trace

“…Radiometric fingerprinting can enhance the security of networks by enabling means of additional authentication based on physical layer properties of devices [5,21,23]. However, we found that an adversary can easily impersonate other devices exploiting the fingerprinting scheme proposed by Liu et al [23].…”

“…We have also witnessed a variety of masquerading attacks in which the adversary mounts a machine-in-themiddle (MitM) attack by creating a rogue access point (AP), mimicking the identity (i.e., SSID) of a legitimate AP. It has been shown that physical layer security, in particular, radiometric (radio frequency) fingerprinting can thwart such attacks [5,21,23].…”

A New Frontier for IoT Security Emerging From Three Decades of Key Generation Relying on Wireless Channels

“…RFF identification has been prototyped in conjunction with a number of wireless techniques, such as WiFi [285], [290], ZigBee [287], [291], Bluetooth [292] and LoRa [293]- [295], just to name a few. Because RFF identification exploits the features of wireless transceivers, it is a perfect candidate for key generation in an integrated security framework [87].…”

Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

“…It should be noted that a great majority of CSI enabled researches depend on limited categories of Network Interface Cards (NICs) for data collection, owing to the limitation of CSI Tools [90]. However, the authors in [102] forward way is to remap signals into the time-frequency domain [103]. In [104], the authors use the STFT (Short-Time Fourier Transform) with the SVM algorithm to identify four different transceivers.…”