RF-Rhythm:Secure and Usable Two-Factor RFID Authentication Jiawei Li*,Chuyu Wangt,Ang Li*,Dianqi Han*,Yan Zhang*,Jinhang Zuof,Rui Zhangs,Lei Xief, Yanchao Zhang* Arizona State University,f Nanjing University,Carnegie Mellon University,University of Delaware [jwli,anglee,dqhan,yanzhangyz,yczhang}@asu.edu,wangcyu217@gmail.com,jzuo@andrew.cmu.edu,ruizhang @udel.edu, Ixie@nju.edu.cn Abstract-Passive RFID technology is widely used in user requires a nontrivial infrastructure update to existing RFID authentication and access control.We propose RF-Rhythm,a systems.Another plausible solution is exploring commercial secure and usable two-factor RFID authentication system with mobile 2FA solutions such as Duo Mobile [2].which require strong resilience to lost/stolen/cloned RFID cards.In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID the RFID user to manually acknowledge an authentication card according to a self-chosen secret melody.Such rhythmic request on his/her enrolled smartphone.This solution needs the taps can induce phase changes in the backscattered signals RFID user to own and always carry a smartphone with good which the RFID reader can detect to recover the user's tapping network connectivity,which may not be feasible in practice. rhythm.In addition to verifying the RFID card's identification information as usual,the backend server compares the extracted We propose RF-Rhythm,a secure and usable two- tapping rhythm with what it acquires in the user enrollment factor RFID authentication system with strong resilience to phase.The user passes authentication checks if and only if both lost/stolen/cloned RFID cards.In RF-Rhythm,each legitimate verifications succeed.We also propose a novel phase-hopping user performs a sequence of taps on his/her RFID card protocol in which the RFID reader emits Continuous Wave(CW) according to a self-chosen secret melody.Such rhythmic taps with random phases for extracting the user's secret tapping can induce phase changes in the backscattered signals,which rhythm.Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from the RFID reader can detect to recover the user's rhythm.In ad- sniffed RFID signals.Comprehensive user experiments confirm dition to verifying the RFID card's identification information the high security and usability of RF-Rhythm with false-positive as usual,the backend server compares the recovered rhythm and false-negative rates close to zero. with what it acquires in the user enrollment phase.The user passes authentication only if both verifications succeed. I.INTRODUCTION The security,usability,and feasibility of RF-Rhythm lie Passive (battery-less)RFID technology has been widely in many aspects.First,a user can easily select a secret yet used in user authentication and access control.An RFID familiar song segment which is very difficult for others to system consists of a backend server,RFID readers,and RFID guess.Second,different users may interpret the same song cards(tags).An RFID reader sends wireless signals to inter- segment in various ways,resulting in diverse rhythmic tap rogate a nearby RFID card,which returns its identification patterns on the card.This means that even if the adversary information by backscattering the reader's signals.The RFID knows the secret song segment,it may still have great difficulty reader then forwards the received information to the backend performing the correct tapping rhythm on the RFID card. server for comparison with the stored information.If a match Third,RF-Rhythm is naturally resilient to traditional replay is found,the RFID user passes authentication and is permitted and relay attacks on RFID authentication systems.Fourth,the to access critical resources or enter a protected area such as a phase information of backscattered signals is readily available business building,parking garage,car,or even home. on commercial RFID readers,so RF-Rhythm only needs a Lost/stolen/cloned RFID cards pose the most critical threat minor software update to the RFID reader and backend system. to RFID authentication systems.In particular,RFID cards are Finally,RF-Rhythm applies to COTS RFID cards and does not often of small size and can be easily lost or stolen;they need the user to carry any other device. can also be cloned with many cheap existing tools.Since Although rhythm-based authentication has been proposed RFID cards are not password-protected,the adversary can for smartphones [3]and smartwatches [4],we are the first to use a lost/stolen/cloned RFID card to pass authentication and explore it in RFID systems and face two unique challenges. impersonate the legitimate user.An effective countermeasure The first challenge is rhythm detection and classification. can be two-factor authentication which requires the RFID i.e.,how to detect and verify the tapping rhythm from user to present the second piece of identification information. noisy RFID signals.In previous work [3],[4].rhythmic taps One such solution requires the RFID user to additionally are directly performed on mobile devices and are fairly easy input a PIN code on a keypad [1].It not only diminishes to detect from inertial sensors.In contrast,rhythmic taps in the convenience of contactless RFID authentication but also RF-Rhythm are performed on the RFID card and have to
RF-Rhythm: Secure and Usable Two-Factor RFID Authentication Jiawei Li∗ , Chuyu Wang† , Ang Li∗ , Dianqi Han∗ , Yan Zhang∗ , Jinhang Zuo‡ , Rui Zhang§ , Lei Xie† , Yanchao Zhang∗ ∗ Arizona State University, † Nanjing University, ‡ Carnegie Mellon University, § University of Delaware {jwli, anglee, dqhan, yanzhangyz, yczhang}@asu.edu, wangcyu217@gmail.com, jzuo@andrew.cmu.edu, ruizhang@udel.edu, lxie@nju.edu.cn Abstract—Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s tapping rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero. I. INTRODUCTION Passive (battery-less) RFID technology has been widely used in user authentication and access control. An RFID system consists of a backend server, RFID readers, and RFID cards (tags). An RFID reader sends wireless signals to interrogate a nearby RFID card, which returns its identification information by backscattering the reader’s signals. The RFID reader then forwards the received information to the backend server for comparison with the stored information. If a match is found, the RFID user passes authentication and is permitted to access critical resources or enter a protected area such as a business building, parking garage, car, or even home. Lost/stolen/cloned RFID cards pose the most critical threat to RFID authentication systems. In particular, RFID cards are often of small size and can be easily lost or stolen; they can also be cloned with many cheap existing tools. Since RFID cards are not password-protected, the adversary can use a lost/stolen/cloned RFID card to pass authentication and impersonate the legitimate user. An effective countermeasure can be two-factor authentication which requires the RFID user to present the second piece of identification information. One such solution requires the RFID user to additionally input a PIN code on a keypad [1]. It not only diminishes the convenience of contactless RFID authentication but also requires a nontrivial infrastructure update to existing RFID systems. Another plausible solution is exploring commercial mobile 2FA solutions such as Duo Mobile [2], which require the RFID user to manually acknowledge an authentication request on his/her enrolled smartphone. This solution needs the RFID user to own and always carry a smartphone with good network connectivity, which may not be feasible in practice. We propose RF-Rhythm, a secure and usable twofactor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the recovered rhythm with what it acquires in the user enrollment phase. The user passes authentication only if both verifications succeed. The security, usability, and feasibility of RF-Rhythm lie in many aspects. First, a user can easily select a secret yet familiar song segment which is very difficult for others to guess. Second, different users may interpret the same song segment in various ways, resulting in diverse rhythmic tap patterns on the card. This means that even if the adversary knows the secret song segment, it may still have great difficulty performing the correct tapping rhythm on the RFID card. Third, RF-Rhythm is naturally resilient to traditional replay and relay attacks on RFID authentication systems. Fourth, the phase information of backscattered signals is readily available on commercial RFID readers, so RF-Rhythm only needs a minor software update to the RFID reader and backend system. Finally, RF-Rhythm applies to COTS RFID cards and does not need the user to carry any other device. Although rhythm-based authentication has been proposed for smartphones [3] and smartwatches [4], we are the first to explore it in RFID systems and face two unique challenges. The first challenge is rhythm detection and classification, i.e., how to detect and verify the tapping rhythm from noisy RFID signals. In previous work [3], [4], rhythmic taps are directly performed on mobile devices and are fairly easy to detect from inertial sensors. In contrast, rhythmic taps in RF-Rhythm are performed on the RFID card and have to
be indirectly extracted from noisy backscattered signals.We Data-0 FMO Preamble explore various signal processing techniques to process noisy raw phase data for extracting a reliable tapping rhythm.We also use machine learning techniques to train a classifier the backend server uses to validate an extracted tapping rhythm. Fig.1.FMO baseband symbols and preamble The second challenge is anti-eavesdropping,i.e.,how to Reader prevent the adversary from acquiring the user's tapping M黑Query黑ACKQueryRep rhythm from sniffed RFID signals.In particular,the ad- T4 人 RN16 versary can easily eavesdrop on the open RFID channel and Harvest power then behave in the same way as the RFID reader to decode the user's tapping rhythm from intercepted RFID signals.It Fig.2.The basic EPC Gen-2 query protocol with a single RFID card. can then repeat the rhythmic taps on lost/stolen/cloned RFID card to successfully impersonate the legitimate user.We tackle 2)The reader sends a Query command followed by CW this challenge by a novel phase-hopping protocol in which of length T1+T2+TRN16.During this CW period,the the RFID reader emits Continuous Wave(CW)with random card backscatters an RN16 message comprising a 6-bit phases for extracting the user's tapping rhythm.Since the preamble,a 16-bit random number,and one dummy bit. adversary does not know the phase-hopping sequence,it can 3)The reader sends an ACK followed by CW of length no longer extract the correct tapping rhythm from sniffed RFID T+T2+TEpC.During this CW period,the card signals. backscatters its EPC (Electronic Product Code). We thoroughly evaluate the security and usability of RF 4)The reader sends QueryRep to finish this query session. Rhythm by comprehensive experiments on Impinj RFID read- EPC Gen-2 [5]gives recommendations for the above timing ers,COTS passive tags,and USRP devices.Our experiments parameters.Let RTcal represent the duration of Interrogator- involve 19 volunteers from two countries and explore three to-Tag calibration symbol,which is specified in the reader representative machine learning techniques,including Support configuration and set to RTcal 72us in our implementation. Vector Machine (SVM).Neural Networks (NN),and Convo- Also let FrT be the frequency tolerance of FMO baseband lutional Neural Networks(CNN).We show that RF-Rhythm signals,which equals 4%for BLF=40 KHz.We have T= is highly secure with false-positive and false-negative rates 2 RTcal=144μsand75us≤T2≤500us.n addition,the close to zero.In addition,we demonstrate the high resilience maximum,minimum,and nominal values of T are 262us, of RF-Rhythm to brute force,visual eavesdropping,and RF 238us,and 250us,respectively. eavesdropping attacks.We also confirm the high usability of RF-Rhythm by a user survey. III.ADVERSARY MODEL II.BASICS OF PASSIVE UHF RFID SYSTEMS We assume an adversary A who attempts to use a lost/stolen/cloned RFID card to pass authentication checks and In this section,we introduce some necessary background about passive Ultra-High-Frequency(UHF)RFID systems.An thus impersonate the legitimate card user.A knows how RF- RFID system consists of a backend server,readers,and RFID Rhythm works and can perform rhythmic taps on the RFID card with fingers or even a fully programmable robotic arm. cards.The RFID reader sends both modulated commands and continuous wave (CW).The RFID card sends back its data We assume that A does not know the legitimate user's secret by exploring the energy harvested from the reader's signals song segment and can try the following attack strategies. to switch its input impedance between two states and thus Brute force:A performs totally random rhythmic taps. modulate the backscattered signal.EPC Gen 2 [5]is the Visual eavesdropping:A observes the legitimate user's most popular UHF RFID standard and assumed throughout tapping behavior,e.g.,by shoulder surfing or a spy the remainder of this paper. camera,and then tries to emulate it. RFID cards encode the backscattered data using either RF eavesdropping:A sniffs all the PHY communication FMO baseband or miller modulation.We only consider FMO traces between the RFID reader and card to recover and encoding in this paper,but our work can easily extend to then perform the legitimate user's rhythmic taps. miller modulation.Fig.I shows the basic FMO symbols.FMO inverts the baseband phase at every symbol boundary with an IV.SYSTEM OVERVIEW additional mid-symbol phase inversion for each data-0.The RF-Rhythm consists of an enrollment phase and a verifica- duration of an FMO symbol is denoted by Tpri 1/BLF, tion phase,and its major modules are depicted in Fig.3, where BLF represents the backscatter link frequency ranging During the enrollment phase,the legitimate user first selects from 40 kHz to 640 kHz [5].To ease our presentation,we an arbitrary song segment familiar to him/herself.Then the assume BLF equal to 40 kHz,corresponding to Tpri 25us. user performs rhythmic taps on his/her RFID card in ac- Fig.2 shows the basic query protocol in EPC Gen-2 [5]. cordance with his/her own interpretation of the chosen song 1)The reader emits CW of length Ta for the RFID card to segment,e.g.,by singing it silently.The user's tapping rhythm harvest and store energy. is referred to as his/her secret rhythm hereafter
be indirectly extracted from noisy backscattered signals. We explore various signal processing techniques to process noisy raw phase data for extracting a reliable tapping rhythm. We also use machine learning techniques to train a classifier the backend server uses to validate an extracted tapping rhythm. The second challenge is anti-eavesdropping, i.e., how to prevent the adversary from acquiring the user’s tapping rhythm from sniffed RFID signals. In particular, the adversary can easily eavesdrop on the open RFID channel and then behave in the same way as the RFID reader to decode the user’s tapping rhythm from intercepted RFID signals. It can then repeat the rhythmic taps on lost/stolen/cloned RFID card to successfully impersonate the legitimate user. We tackle this challenge by a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s tapping rhythm. Since the adversary does not know the phase-hopping sequence, it can no longer extract the correct tapping rhythm from sniffed RFID signals. We thoroughly evaluate the security and usability of RFRhythm by comprehensive experiments on Impinj RFID readers, COTS passive tags, and USRP devices. Our experiments involve 19 volunteers from two countries and explore three representative machine learning techniques, including Support Vector Machine (SVM), Neural Networks (NN), and Convolutional Neural Networks (CNN). We show that RF-Rhythm is highly secure with false-positive and false-negative rates close to zero. In addition, we demonstrate the high resilience of RF-Rhythm to brute force, visual eavesdropping, and RF eavesdropping attacks. We also confirm the high usability of RF-Rhythm by a user survey. II. BASICS OF PASSIVE UHF RFID SYSTEMS In this section, we introduce some necessary background about passive Ultra-High-Frequency (UHF) RFID systems. An RFID system consists of a backend server, readers, and RFID cards. The RFID reader sends both modulated commands and continuous wave (CW). The RFID card sends back its data by exploring the energy harvested from the reader’s signals to switch its input impedance between two states and thus modulate the backscattered signal. EPC Gen 2 [5] is the most popular UHF RFID standard and assumed throughout the remainder of this paper. RFID cards encode the backscattered data using either FM0 baseband or miller modulation. We only consider FM0 encoding in this paper, but our work can easily extend to miller modulation. Fig. 1 shows the basic FM0 symbols. FM0 inverts the baseband phase at every symbol boundary with an additional mid-symbol phase inversion for each data-0. The duration of an FM0 symbol is denoted by Tpri = 1/BLF, where BLF represents the backscatter link frequency ranging from 40 kHz to 640 kHz [5]. To ease our presentation, we assume BLF equal to 40 kHz, corresponding to Tpri = 25µs. Fig. 2 shows the basic query protocol in EPC Gen-2 [5]. 1) The reader emits CW of length T4 for the RFID card to harvest and store energy. Data-0 Data-1 Tpri 1 0 1 0 v 1 FM0 Preamble Fig. 1. FM0 baseband symbols and preamble. Query RN16 ACK QueryRep EPC Reader Tag T1 T2 T1 T2 CW T4 Harvest power CW CW Fig. 2. The basic EPC Gen-2 query protocol with a single RFID card. 2) The reader sends a Query command followed by CW of length T1 + T2 + TRN16. During this CW period, the card backscatters an RN16 message comprising a 6-bit preamble, a 16-bit random number, and one dummy bit. 3) The reader sends an ACK followed by CW of length T1 + T2 + TEPC. During this CW period, the card backscatters its EPC (Electronic Product Code). 4) The reader sends QueryRep to finish this query session. EPC Gen-2 [5] gives recommendations for the above timing parameters. Let RTcal represent the duration of Interrogatorto-Tag calibration symbol, which is specified in the reader configuration and set to RTcal = 72µs in our implementation. Also let FrT be the frequency tolerance of FM0 baseband signals, which equals 4% for BLF = 40 KHz. We have T4 = 2RTcal = 144µs and 75µs ≤ T2 ≤ 500µs. In addition, the maximum, minimum, and nominal values of T1 are 262µs, 238µs, and 250µs, respectively. III. ADVERSARY MODEL We assume an adversary A who attempts to use a lost/stolen/cloned RFID card to pass authentication checks and thus impersonate the legitimate card user. A knows how RFRhythm works and can perform rhythmic taps on the RFID card with fingers or even a fully programmable robotic arm. We assume that A does not know the legitimate user’s secret song segment and can try the following attack strategies. • Brute force: A performs totally random rhythmic taps. • Visual eavesdropping: A observes the legitimate user’s tapping behavior, e.g., by shoulder surfing or a spy camera, and then tries to emulate it. • RF eavesdropping: A sniffs all the PHY communication traces between the RFID reader and card to recover and then perform the legitimate user’s rhythmic taps. IV. SYSTEM OVERVIEW RF-Rhythm consists of an enrollment phase and a verification phase, and its major modules are depicted in Fig. 3, During the enrollment phase, the legitimate user first selects an arbitrary song segment familiar to him/herself. Then the user performs rhythmic taps on his/her RFID card in accordance with his/her own interpretation of the chosen song segment, e.g., by singing it silently. The user’s tapping rhythm is referred to as his/her secret rhythm hereafter
Random phase 了WM RFID opping Sequence Generator Reader Anti-eavesdropping RFID Protocol Phase EPC Rhythm Detection Signal Feature Rhythm Learning/ Processing Matching Extraction Classification Fig.4.Absolute phase changes induced by rhythmic taps on an RFID card. Backend Server 2 02 Fig.3.The RF-Rhythm system flowchart. 0.1 ease The security of RF-Rhythm relies on the secrecy of the chosen song segment and also the user's likely unique tapping -0.1 rhythm.In particular,since there are numerous songs available, 0 the adversary can hardly guess the selected song segment of a 9.0 Time (s) 92 9.3 8.9 9.0 9.2 9.3 target user;an advanced user such as a musician can even self- (a)Absolute phase (b)Phase difference compose the song segment.In addition,people may have very Fig.5.Absolute and differential phase changes caused by a single tap. subjective mental interpretations about the same song segment, resulting in totally different tapping rhythms. just be replaced by a cryptographic authentication message. The backend server handles the enrollment request as fol- We ignore this option henceforth for ease of illustration. lows.First,it acquires the EPC of the user's RFID card through the reader as usual by using the protocol in Fig.2.Second,it V.RF-RHYTHM DESIGN DETAILS instructs the user to perform rhythmic taps on the RFID card, A.Feasibility Study:Tap Detection which would lead to phase changes in the backscattered signals received by the reader.Third,the server invokes a Signal The backscattered signal's phase is available on commercial Processing module to extract reliable phase data from noisy RFID readers such as Impinj R420 [6].According to [7],it backscattered signals.Fourth,it uses a Feature Extraction can be expressed as(+rader+cd)mod 2 module to obtain a feature vector that characterizes the use's where 2d is the round-trip propagation distance between the tapping rhythm.Finally,it asks the user to repeat the rhythmic reader and card,f is the CW frequency,cis the speed of light, taps multiple times and then feeds all the resulting feature dreader denotes the phase rotation due to the reader's transmit vectors into a Rhythm Learning module to train a high-quality and receive circuits,and card represents the phase rotation binary rhythm classifier for this user. caused by the RFID card's reflection characteristics. In the verification phase,the backend server first explores Finger taps on the RFID card can change its circuit the RFID card for its EPC with the protocol in Fig.2.If the impedance and also signal propagation,leading to some addi- EPC is found in the database,the server instructs the reader to tional phase rotation denoted by tap So we modify the phase execute multiple rounds of the protocol again in Fig.2.RF- expression above to Rhythm is highly usable in the sense that the RFID user just needs to perform his/her secret tapping rhythm multiple times (ddd+upmod 2. (1) 、c without the need to know when the server starts to extract it in both the enrollment and verification phases.The server invokes To better understand the effect of finger taps,we per- the same Signal Processing and Feature Extraction modules to form a simple experiment using a Impinj R420 reader and extract a candidate tapping rhythm in each round,which is then a SMARTRAC R6 DogBone tag.Fig.4 shows the phase tested with the trained rhythm classifier associated with the changes induced by rhythmic finger taps on the RFID card EPC acquired before.The authentication process terminates in accordance with the shown song segment.We also show until when the server either detects a valid tapping rhythm or the phase change associated with a single tap in Fig.5.A fails to detect one after a threshold number of rounds.The tap event can be decomposed into a press stage and a release RFID card and corresponding user are considered authentic in stage.So we use [tpress,trelease]to represent a tap event in the the former case and fake in the latter. time domain,where tpress and trelease denote the time that the RF-Rhythm features a novel anti-eavesdropping protocol phase (difference)starts to change and return to the baseline employed by the RFID reader to emit CW with random value,respectively.Fig.5a and Fig.5b depict the absolute phases for extracting the user's secret tapping rhythm in both phase values and the difference between adjacent phase values, enrollment and verification phases.Our protocol can prevent respectively.These results clearly demonstrate the feasibility a capable adversary from recovering and then replaying the of exploring phase changes for tap detection. legitimate user's secret rhythm from sniffed RFID signals. Our descriptions above focus on very cheap COTS RFID B.Data Processing cards and can also be easily adapted to more powerful,ex- We represent the reader's phase report at time ti by pensive cryptographic RFID cards.For example,the EPC can fi,ti],where fi denotes the CW frequency atti.Accord-
Feature Extraction Rhythm Learning/ Classification Matching Rhythm Detection Anti-eavesdropping RFID Protocol Signal Processing Random Phase Hopping Sequence Generator RFID Reader Random Phase Hopping Sequence Generator RFID Reader Random Phase Hopping Sequence Generator RFID Reader Backend Server Phase Fig. 3. The RF-Rhythm system flowchart. The security of RF-Rhythm relies on the secrecy of the chosen song segment and also the user’s likely unique tapping rhythm. In particular, since there are numerous songs available, the adversary can hardly guess the selected song segment of a target user; an advanced user such as a musician can even selfcompose the song segment. In addition, people may have very subjective mental interpretations about the same song segment, resulting in totally different tapping rhythms. The backend server handles the enrollment request as follows. First, it acquires the EPC of the user’s RFID card through the reader as usual by using the protocol in Fig. 2. Second, it instructs the user to perform rhythmic taps on the RFID card, which would lead to phase changes in the backscattered signals received by the reader. Third, the server invokes a Signal Processing module to extract reliable phase data from noisy backscattered signals. Fourth, it uses a Feature Extraction module to obtain a feature vector that characterizes the use’s tapping rhythm. Finally, it asks the user to repeat the rhythmic taps multiple times and then feeds all the resulting feature vectors into a Rhythm Learning module to train a high-quality binary rhythm classifier for this user. In the verification phase, the backend server first explores the RFID card for its EPC with the protocol in Fig. 2. If the EPC is found in the database, the server instructs the reader to execute multiple rounds of the protocol again in Fig. 2. RFRhythm is highly usable in the sense that the RFID user just needs to perform his/her secret tapping rhythm multiple times without the need to know when the server starts to extract it in both the enrollment and verification phases. The server invokes the same Signal Processing and Feature Extraction modules to extract a candidate tapping rhythm in each round, which is then tested with the trained rhythm classifier associated with the EPC acquired before. The authentication process terminates until when the server either detects a valid tapping rhythm or fails to detect one after a threshold number of rounds. The RFID card and corresponding user are considered authentic in the former case and fake in the latter. RF-Rhythm features a novel anti-eavesdropping protocol employed by the RFID reader to emit CW with random phases for extracting the user’s secret tapping rhythm in both enrollment and verification phases. Our protocol can prevent a capable adversary from recovering and then replaying the legitimate user’s secret rhythm from sniffed RFID signals. Our descriptions above focus on very cheap COTS RFID cards and can also be easily adapted to more powerful, expensive cryptographic RFID cards. For example, the EPC can Fig. 4. Absolute phase changes induced by rhythmic taps on an RFID card. 0 π/2 π 3π/2 2π 8.9 9.0 9.1 9.2 9.3 tpress t release Phase Time (s) (a) Absolute phase -0.2 -0.1 0 0.1 0.2 8.9 9.0 9.1 9.2 9.3 tpress t release Phase Time (s) (b) Phase difference Fig. 5. Absolute and differential phase changes caused by a single tap. just be replaced by a cryptographic authentication message. We ignore this option henceforth for ease of illustration. V. RF-RHYTHM DESIGN DETAILS A. Feasibility Study: Tap Detection The backscattered signal’s phase is available on commercial RFID readers such as Impinj R420 [6]. According to [7], it can be expressed as φ = ( 4πdf c + φreader + φcard) mod 2π, where 2d is the round-trip propagation distance between the reader and card, f is the CW frequency, c is the speed of light, φreader denotes the phase rotation due to the reader’s transmit and receive circuits, and φcard represents the phase rotation caused by the RFID card’s reflection characteristics. Finger taps on the RFID card can change its circuit impedance and also signal propagation, leading to some additional phase rotation denoted by φtap. So we modify the phase expression above to φ = 4πdf c + φreader + φcard + φtap mod 2π. (1) To better understand the effect of finger taps, we perform a simple experiment using a Impinj R420 reader and a SMARTRAC R6 DogBone tag. Fig. 4 shows the phase changes induced by rhythmic finger taps on the RFID card in accordance with the shown song segment. We also show the phase change associated with a single tap in Fig. 5. A tap event can be decomposed into a press stage and a release stage. So we use [tpress, trelease] to represent a tap event in the time domain, where tpress and trelease denote the time that the phase (difference) starts to change and return to the baseline value, respectively. Fig. 5a and Fig. 5b depict the absolute phase values and the difference between adjacent phase values, respectively. These results clearly demonstrate the feasibility of exploring phase changes for tap detection. B. Data Processing We represent the reader’s phase report at time ti by [φi , fi , ti ], where fi denotes the CW frequency at ti . Accord-
ing to Eq.(1),we have the parenthesis from A.Instead,we compute the time- normalized phase difference for t;as p:= (dreader+andup. mod 2, (2) △p:=(△p+1+△p-1) ti-ti-1 tit1-ti-1 (5) where tap.i denotes the phase shift during the ith tap.The intervalt+1-ti(i>0)is about 4ms on the Impinj R420 Fig.6b plots the output of the Data Processing module corresponding to Fig.6a after we adopt the above technique. reader.We temporarily assume that fi is constant and perform the following steps to process the raw phase data to extract more useful information for further rhythm extraction. 01 Phase difference and unwrapping.We use the phase dif- 0.05 ference instead of the absolute phase to eliminate the ap- proximately constantd during adjacent -0.05 tap events.In addition,the raw phase data are wrapped within [0,2],so it is critical to perform phase unwrapping 6000 7500 8000 -0.1 8000 to eliminate ambiguity.Our experiments reveal that although (a)Raw phase with frequency hopping (b)Processed phase difference the phase change induced by tap events are sharp,it is always Fig.6.Data processing under frequency hopping. bounded by m.According to this finding,the unwrapped phase difference is calculated by D.Feature Extraction Since a tapping rhythm consists of individual taps and tap- 0-p-1, -n durations,we first seek to extract individual tap events from the △pi=pap,i-pap,i-1 p:-p-1+2r,p:-pi-1<-7 processed phase data④=[△pi,△p2,.,△pN].Recall that -1-2m,-1>n each tap event can be represented by [tpress,treleasel.We draw (3) three observations from Fig.5b obtained from preliminary Here n is an empirical value set to 3.5 in this paper. experiments.First,the start and end of a tap event correspond Normalization.Since the sampling rate of the RFID reader is to the phase difference beginning to deviate from and return not consistent,so we further derive the time-normalized phase to the zero baseline,respectively.Second,the phase difference difference as first decreases from and then returns to the zero baseline △:= △p: △p: when the user finger goes from just touching to fully pressing △ti (4) on the RFID card,leading to a local minimum.Finally,the ti-ti-1 phase difference first increases from and then returns to the Interpolation and filtering.We further use a linear interpo- zero baseline when the user finger goes from decreasing the lation with a factor of 4 and a 15-point average value filter pressure on to completely leaving the RFID card,resulting in to smooth the data and also mitigate the noise.We denote the a local maximum.The later two observations are both because final smoothed data by④=[△p1,△p2,.,△pwl,where N the card impedance gradually change with the finger pressure denotes the total number of data points. on the card during a tap event.Armed with these observations, we use the following empirical process C.Mitigating Frequency Hopping 1)Find all the local maximums above 6 and minimums below o2inΦ. We intend RF-Rhythm to be a universal solution worldwide 2)Pair each local minimum with the immediate local and thus must deal with frequency hopping mandated in many maximum (if any)such that there are no other local regions.For example,FCC requires that all RFID readers used minimums or maximums in between.We require the in the US apply frequency hopping across 50 channels ranging user's tapping rhythm to be sufficiently long such that from 902 to 928 MHz with the dwell time on each interval no M>2 local minimum-maximum pairs can be located larger than 0.4 seconds.According to Eq.(2),such frequency inΦ，each associated with a unique tap event. hopping naturally leads to phase discontinuity in Fig.6a. 3)Find the first data point before (after)the local minimum To see the effect of frequency hopping more clearly,assume (maximum)which is within tg from the zero baseline that frequency hopping occurs atti(>2).In the Impinj for each local minim-maximum pair.The corresponding R420 reader,the frequency-hopping interval is 200ms,while timestamp is used as tpress (trelease)of the tap event. the phase-report interval is about 4ms.So there is no frequency The thresholds 61,62,and 63 can be obtained empirically hopping at ti-2.ti-1,and ti+1,i.e.,fi-2 =fi-1 fi=fi+1. The phase difference in Eq.(3)is in effect through experiments. Finally,we obtain an M-tap event sequence as △o=Ap-p-1+(红d-4红d班】 V= tpress,1 tpress,2 ..tpress,M (6) trelcase,1 trelease,2 ..trelease,M Since d is unknown and hard to estimate in practice,we from which we can derive a feature vector F cannot do a simple calibration by subtracting the term in [F1,...,FM-1],where Fi=tpress.+1-trelease.i
ing to Eq. (1), we have φi = 4πdfi c + φreader + φcard + φtap,i mod 2π , (2) where φtap,i denotes the phase shift during the ith tap. The interval ti+1 − ti (i ≥ 0) is about 4ms on the Impinj R420 reader. We temporarily assume that fi is constant and perform the following steps to process the raw phase data to extract more useful information for further rhythm extraction. Phase difference and unwrapping. We use the phase difference instead of the absolute phase to eliminate the approximately constant 4πdfi c + φreader + φcard during adjacent tap events. In addition, the raw phase data are wrapped within [0, 2π], so it is critical to perform phase unwrapping to eliminate ambiguity. Our experiments reveal that although the phase change induced by tap events are sharp, it is always bounded by π. According to this finding, the unwrapped phase difference is calculated by ∆φi = φtap,i−φtap,i−1 = φi − φi−1, |φi − φi−1| ≤ η φi − φi−1 + 2π, φi − φi−1 < −η φi − φi−1 − 2π, φi − φi−1 > η (3) Here η is an empirical value set to 3.5 in this paper. Normalization. Since the sampling rate of the RFID reader is not consistent, so we further derive the time-normalized phase difference as ∆φi = ∆φi ∆ti = ∆φi ti − ti−1 . (4) Interpolation and filtering. We further use a linear interpolation with a factor of 4 and a 15-point average value filter to smooth the data and also mitigate the noise. We denote the final smoothed data by Φ = [∆φ1, ∆φ2, . . . , ∆φN ], where N denotes the total number of data points. C. Mitigating Frequency Hopping We intend RF-Rhythm to be a universal solution worldwide and thus must deal with frequency hopping mandated in many regions. For example, FCC requires that all RFID readers used in the US apply frequency hopping across 50 channels ranging from 902 to 928 MHz with the dwell time on each interval no larger than 0.4 seconds. According to Eq. (2), such frequency hopping naturally leads to phase discontinuity in Fig. 6a. To see the effect of frequency hopping more clearly, assume that frequency hopping occurs at ti (i ≥ 2). In the Impinj R420 reader, the frequency-hopping interval is 200ms, while the phase-report interval is about 4ms. So there is no frequency hopping at ti−2, ti−1, and ti+1, i.e., fi−2 = fi−1 6= fi = fi+1. The phase difference in Eq. (3) is in effect ∆φi = φtap,i − φtap,i−1 + 4πdfi c − 4πdfi−1 c . Since d is unknown and hard to estimate in practice, we cannot do a simple calibration by subtracting the term in the parenthesis from ∆φi . Instead, we compute the timenormalized phase difference for ti as ∆φi = (∆φi+1 + ∆φi−1) ti − ti−1 ti+1 − ti−1 (5) Fig. 6b plots the output of the Data Processing module corresponding to Fig. 6a after we adopt the above technique. 0 π/2 π 3π/2 2π 6000 6500 7000 7500 8000 Phase Time (ms) (a) Raw phase with frequency hopping -0.1 -0.05 0 0.05 0.1 6000 6500 7000 7500 8000 Phase Time (ms) (b) Processed phase difference Fig. 6. Data processing under frequency hopping. D. Feature Extraction Since a tapping rhythm consists of individual taps and tapdurations, we first seek to extract individual tap events from the processed phase data Φ = [∆φ1, ∆φ2, . . . , ∆φN ]. Recall that each tap event can be represented by [tpress, trelease]. We draw three observations from Fig. 5b obtained from preliminary experiments. First, the start and end of a tap event correspond to the phase difference beginning to deviate from and return to the zero baseline, respectively. Second, the phase difference first decreases from and then returns to the zero baseline when the user finger goes from just touching to fully pressing on the RFID card, leading to a local minimum. Finally, the phase difference first increases from and then returns to the zero baseline when the user finger goes from decreasing the pressure on to completely leaving the RFID card, resulting in a local maximum. The later two observations are both because the card impedance gradually change with the finger pressure on the card during a tap event. Armed with these observations, we use the following empirical process 1) Find all the local maximums above δ1 and minimums below δ2 in Φ. 2) Pair each local minimum with the immediate local maximum (if any) such that there are no other local minimums or maximums in between. We require the user’s tapping rhythm to be sufficiently long such that M 2 local minimum-maximum pairs can be located in Φ, each associated with a unique tap event. 3) Find the first data point before (after) the local minimum (maximum) which is within ±δ3 from the zero baseline for each local minim-maximum pair. The corresponding timestamp is used as tpress (trelease) of the tap event. The thresholds δ1, δ2, and δ3 can be obtained empirically through experiments. Finally, we obtain an M-tap event sequence as V = tpress,1 tpress,2 . . . tpress,M trelease,1 trelease,2 . . . trelease,M , (6) from which we can derive a feature vector F = [F1, . . . , FM−1], where Fi = tpress,i+1 − trelease,i
Data-0 CW phase shift /6/3 Fig.7.Complex demodulated signals received by the reader. (a) (b) E.Rhythm Classification Fig.8.Illustration of reader-phase hopping The backend server builds a rhythm classifier during the enrollment phase.To do so,it instructs the user to perform it can carefully study the tapping rhythm and reproduce it rhythmic taps in accordance with his/her secret song segment by hand or even through a programmable robotic arm on the lost/stolen/cloned RFID card.Since this attack directly multiple times.The resulting phase-difference vectors may vary due to slight tapping variations.So we apply Dynamic exploits physical-layer RFID signals,it cannot be thwarted by Time Warping (DTW)[8]to align all the phase-difference encrypting RFID protocol messages at the application layer. vectors to that of the first acquired tapping rhythm.Then we B.Phase Hopping to Mitigate Rhythm Eavesdropping obtain a feature vector from each aligned phase-difference We propose to let the RFID reader emit CW with random vector and pad zeros in the end (if needed)to make all the phases to counteract the rhythm-eavesdropping attack.The feature vectors have the same length.Finally,we use the objective is to prevent the adversary from obtaining matching resulting feature vectors to train a rhythm classifier based symbols in states S1 and S2,so it cannot derive the correct an any established machine learning technique.We compare phases of backscattered signals as in Fig.7. the performance of one-vs-all linear Support Vector Machine Fig.8 explains the intuition of our defense.Assume that (SVM),Neural Networks (NN),and Convolutional Neural the RFID card is backscattering a data-0 symbol.As said Networks (CNN)in Section VII.During each authentication above,the card only backscatters the high-voltage part.As session,the server explores the same processes to extract a shown in Fig.8a,we let the reader set the CW phases to /6 tapping rhythm and then test it with the rhythm classifier. and /3 during backscattering and non-backscattering,respec- VI.ANTI-EAVESDROPPING VIA PHASE HOPPING tively.The adversary again tries to cluster sniffed symbols into states S1 and S2.Due to phase hopping,the S1 symbols that A.Rhythm-Eavesdropping Attack correspond to non-backscattering has a phase offset of /3, We first explain the principle with which the RFID reader labeled by SI'in Fig.8b.The true S1 symbol matching the S2 extracts the signals backscattered by the RFID card.As shown symbol,however,should have a phase offset of /6,labeled in Fig.1,there are two possible voltage levels in FMO symbols. by S1 in Fig.8b.Since the adversary does not know the true The card only backscatters when transmitting high-voltage CW phase during backscattering,it can only use the symbols pulses.Consider the query protocol in Fig.2.The symbols in SI'and S2 to derive a wrong phase o.But the reader received by the reader between its two consecutive commands knows the true CW phase or S1 symbol and can thus derive (e.g..Query and ACK)can be classified into two states (SI the correct phase o. and S2).The symbols in SI contain only constant CW,while those in S2 are the superposition of CW and backscattered C.Protocol Design signals.For simplicity,we represent the symbols in SI and It is very challenging to properly implement the phase- S2 by two single points in the complex I-Q plane in Fig.7. hopping idea above.In particular,our example in Fig.8 corresponding to vector VL and VB,respectively.The phase assumes perfect reader-tag synchronization such that the reader of backscattered signals can be derived as [9] knows exactly when backscattering occurs and thus when to change the CW phase.This assumption is impossible to hold 馆匠 o=arccos (7) in practice.Therefore,the adversary may still be able to obtain matching symbols in SI and S2 to derive the correct phase and eventually the legitimate tapping rhythm.A tempting solution The phase reports from the reader correspond to the samples of is using a very short hopping interval,which nevertheless o above.As said,the phase-sampling frequency in the Impinj may negatively affect the reader's capability to recover the R420 reader is about 4ms. correct phase and thus the tapping rhythm.It is thus critical To launch the rhythm-eavesdropping attack,the adversary to determine the optimal phase-hopping interval to strike a can just passively sniff the reader-card communications with balance between attack resilience and system correctness. its own RFID reader or a software-defined radio.After clas- We illustrate our phase-hopping protocol with a simplified sifying sniffed symbols into SI and S2,it uses the same version of the query protocol in Fig.2.Assume that the process above to extract o.Next,it explores the workflow backend server acquires and validates the card's EPC with the in Section V to acquire the legitimate tapping rhythm.Finally, protocol in Fig.2.It then instructs the RFID reader to initiate
I Q S1 S2 VL VB Fig. 7. Complex demodulated signals received by the reader. E. Rhythm Classification The backend server builds a rhythm classifier during the enrollment phase. To do so, it instructs the user to perform rhythmic taps in accordance with his/her secret song segment multiple times. The resulting phase-difference vectors may vary due to slight tapping variations. So we apply Dynamic Time Warping (DTW) [8] to align all the phase-difference vectors to that of the first acquired tapping rhythm. Then we obtain a feature vector from each aligned phase-difference vector and pad zeros in the end (if needed) to make all the feature vectors have the same length. Finally, we use the resulting feature vectors to train a rhythm classifier based an any established machine learning technique. We compare the performance of one-vs-all linear Support Vector Machine (SVM), Neural Networks (NN), and Convolutional Neural Networks (CNN) in Section VII. During each authentication session, the server explores the same processes to extract a tapping rhythm and then test it with the rhythm classifier. VI. ANTI-EAVESDROPPING VIA PHASE HOPPING A. Rhythm-Eavesdropping Attack We first explain the principle with which the RFID reader extracts the signals backscattered by the RFID card. As shown in Fig. 1, there are two possible voltage levels in FM0 symbols. The card only backscatters when transmitting high-voltage pulses. Consider the query protocol in Fig. 2. The symbols received by the reader between its two consecutive commands (e.g., Query and ACK) can be classified into two states (S1 and S2). The symbols in S1 contain only constant CW, while those in S2 are the superposition of CW and backscattered signals. For simplicity, we represent the symbols in S1 and S2 by two single points in the complex I-Q plane in Fig. 7, corresponding to vector V~L and V~B, respectively. The phase of backscattered signals can be derived as [9] φ = arccos( V~B · V~L V~B V~B ). (7) The phase reports from the reader correspond to the samples of φ above. As said, the phase-sampling frequency in the Impinj R420 reader is about 4ms. To launch the rhythm-eavesdropping attack, the adversary can just passively sniff the reader-card communications with its own RFID reader or a software-defined radio. After classifying sniffed symbols into S1 and S2, it uses the same process above to extract φ. Next, it explores the workflow in Section V to acquire the legitimate tapping rhythm. Finally, Data-0 CW phase shift π/6 π/3 (a) I Q S1' S2 VL2 S1 VB ' VB ' VL1 (b) Fig. 8. Illustration of reader-phase hopping. it can carefully study the tapping rhythm and reproduce it by hand or even through a programmable robotic arm on the lost/stolen/cloned RFID card. Since this attack directly exploits physical-layer RFID signals, it cannot be thwarted by encrypting RFID protocol messages at the application layer. B. Phase Hopping to Mitigate Rhythm Eavesdropping We propose to let the RFID reader emit CW with random phases to counteract the rhythm-eavesdropping attack. The objective is to prevent the adversary from obtaining matching symbols in states S1 and S2, so it cannot derive the correct phases of backscattered signals as in Fig. 7. Fig. 8 explains the intuition of our defense. Assume that the RFID card is backscattering a data-0 symbol. As said above, the card only backscatters the high-voltage part. As shown in Fig. 8a, we let the reader set the CW phases to π/6 and π/3 during backscattering and non-backscattering, respectively. The adversary again tries to cluster sniffed symbols into states S1 and S2. Due to phase hopping, the S1 symbols that correspond to non-backscattering has a phase offset of π/3, labeled by S10 in Fig. 8b. The true S1 symbol matching the S2 symbol, however, should have a phase offset of π/6, labeled by S1 in Fig. 8b. Since the adversary does not know the true CW phase during backscattering, it can only use the symbols in S10 and S2 to derive a wrong phase φ 0 . But the reader knows the true CW phase or S1 symbol and can thus derive the correct phase φ. C. Protocol Design It is very challenging to properly implement the phasehopping idea above. In particular, our example in Fig. 8 assumes perfect reader-tag synchronization such that the reader knows exactly when backscattering occurs and thus when to change the CW phase. This assumption is impossible to hold in practice. Therefore, the adversary may still be able to obtain matching symbols in S1 and S2 to derive the correct phase and eventually the legitimate tapping rhythm. A tempting solution is using a very short hopping interval, which nevertheless may negatively affect the reader’s capability to recover the correct phase and thus the tapping rhythm. It is thus critical to determine the optimal phase-hopping interval to strike a balance between attack resilience and system correctness. We illustrate our phase-hopping protocol with a simplified version of the query protocol in Fig. 2. Assume that the backend server acquires and validates the card’s EPC with the protocol in Fig. 2. It then instructs the RFID reader to initiate