GPS Time Authentication against Spoofingvia a Network of Receivers for Power Systems

Sriramya Bhamidipati Student Member, IEEE, Tara Yasmin Mina, Student Member, IEEEand Grace Xingxin Gao Senior Member, IEEE

Abstract— Due to the unencrypted structure of civil GPSsignals, the timing information supplied to the PMUs in thepower grid is vulnerable to spoofing attacks. We propose ourGPS time authentication algorithm using a network of widelydispersed static receivers and their known positions. Withoutrequiring the knowledge of the exact P(Y) code sequences, wecross-check for the presence of these encrypted codes acrossthe receivers to detect spoofing attacks.

First, we perform pair-wise cross-correlation of the con-ditioned quadrature-phase, carrier wiped-off incoming signalacross the receivers. Later, we utilize position-informationaiding to estimate the expected time offset between the receivedP(Y) codes at different receivers. Thereafter, we authenticateeach receiver by analyzing the weighted summation of the pair-wise cross-correlation peak offset and magnitude across thereceivers and their common satellites.

To validate our networked spoofing detection algorithm, weutilize four GPS receivers located in Idaho, Illinois, Coloradoand Ohio. Under the presence of simulated spoofing attacks,namely signal-level spoofing and a record-and-replay attack, wedemonstrate that our networked approach successfully detectsthese spoofing events with high probability.

I. INTRODUCTION

The push to modernize the U.S. power grid by recentlegislation [1] has led to an increased investment in theinstallation of Phasor Measurement Units (PMUs). PMUsmonitor the power grid by recording time-tagged voltageand current phasors at critical substations. In particular,the American Recovery and Reinvestment Act of 2009 [2]prompted the installation of over 1000 additional PMUs froma mere 166 before the legislation was passed. Fig. 1 showsthe widely dispersed and geographically distributed networkof PMUs [3] across the U.S.

To synchronize this network, each PMU timestamps themeasured voltage and current phasors with the precise timeestimated by a GPS receiver [4]. GPS has advantages ofproviding microsecond-level accuracy in estimating Uni-versal Time Coordinated (UTC) time, has global coverageand is freely available to all users. However, because thecivilian GPS signal structure and relevant code sequencesare publicly available [5], the timing information supplied toPMUs is vulnerable to external spoofing attacks [6].

Some of the GPS spoofing attacks which threaten thestability of the power systems include:

1) Meaconing: A spoofer executing a meaconing at-tack (also known as a record-and-replay) as seen inFig. 2(a), records the GPS signals and replays them

at a later time to overpower the authentic signals andmanipulate the receiver [7]. To execute this attack, thespoofer does not require knowledge of the encryptedcodes and is capable of spoofing even military re-ceivers.

2) Signal-level spoofing: This is a sophisticated three-stage attack as shown in Fig. 2(b). At first, a spoofergenerates and broadcasts counterfeit GPS signals iden-tical to the authentic signals received at the targetreceiver [8]. In the second stage, the power of thesemalicious signals is slowly increased to mislead thetarget receiver to lock onto these counterfeit signals.Once locked, the spoofer manipulates the time bymoving the counterfeit signal away from the truetime. Since there is no sudden change in the GPStiming output, this method cannot easily be detectedby traditional methods.

Fig. 1: Geographically distributed PMUs deployed across theU.S to monitor the power grid stability [9].

Previous research [10]-[12] performed GPS signal authen-tication of a user receiver via cross-correlation of sampledmilitary signals against a reference receiver. While the au-thors provided a strong validation of the concept, the cross-check relies on one reference receiver to be secure at alltimes. In case the reference receiver or the communicationlink is compromised, the spoofing detection is also compro-mised. Our prior work [13] has demonstrated an increasedprobability of signal-level spoofing detection for a generalframework by utilizing a small number of distributed, low-cost receivers as opposed to one reference station.

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(a) Meaconing attack (b) Signal-level spoofing attack

Fig. 2: Types of spoofing attacks: (a) meaconing attackinvolving record and replay of GPS signals; (b) signal-levelspoofing that generates spoofing signals to trick the receiverinto latching onto the spoofing peak.

In our current work, we cross-check the encrypted signa-ture across a network of geographically distributed receiversto analyze the spoofing status of each receiver in the powersystems. In addition, we utilize the known position of staticGPS receivers to develop a two-stage authentication proce-dure which not only detects signal-level spoofing, that cannotgenerate P(Y) codes but also a record-and-replay attack,which transmits a delayed P(Y) code. We leverage the exist-ing power grid resources, namely the secure communicationchannels and a centralized power grid monitoring system, tocollectively cross-check the GPS snippets across receivers.

The rest of the paper is organized as follows: Section IIdescribes our architecture and its key characteristics; Sec-tion III explains our networked spoofing detection algorithmin detail; Section IV experimentally validates the successfuldetection of simulated spoofing attacks, namely record-and-replay and signal-level spoofing, using four geographicallydistributed receivers; Section V concludes the paper.

II. OUR ARCHITECTURE

In this section, we describe the various aspects consideredin our networked spoofing detection algorithm, as outlined inFig. 3. Our proposed algorithm adds an additional protectionlayer to our prior proposed receiver algorithms [14]-[15]in providing robust GPS timing for PMUs. The keycharacteristics of our algorithm are as follows:

1. Widely dispersed network of receivers:By leveraging the geographically distributed architecture

of the U.S power grid as described in Section I, we considera handful (4-8) of GPS receivers situated at widely dispersedlocations. This is because the probability of these widelydispersed receivers being spoofed by the same attacker andat the same time is minimal. In addition, this also eliminatesthe need for a secure receiver to be present as a referencefor cross-checking.

2. Utilizing encrypted P(Y) codes:The presence of P(Y) codes in the quadrature-phase of

L1 C/A GPS signals serves as a unique authenticationsignature that cannot be forged by a spoofer because of itsencrypted nature. Without requiring the knowledge of these

exact sequences, we utilize the conditioned GPS snippetto authenticate the receivers via pair-wise cross-correlationsperformed among a network of receivers.

Fig. 3: System architecture of our proposed approach. Ourapproach utilizes the component of the GPS signal consistingof encrypted military P(Y) codes as a unique authenticationsignature, without requiring knowledge of the exact P(Y)code sequences. We further take advantage of the existingcommunication network between PMU stations to leveragethe geographical redundancy of multiple PMU sites.

3. Position Information Aiding:Given that each power substation is static, we pre-compute

the position (Xk = [x, y, z]known, k = 1, . . . ,M) of our Mwidely distributed network of receivers. The existing powergrid network provides PMUs with satellite ephemeris datafrom external sources, thus we can utilize this availableinformation to obtain the accurate satellite position Si =[xs, ys, zs], i = 1, . . . , N of the N satellites in view. Thisposition information aiding is especially useful in detectinga meaconed signal, which contains the P(Y) codes but at adelayed received time. By using the known receiver posi-tions, we compute the expected offset between the receivedP(Y) codes at different PMU sites. The consistency betweenthe expected offsets using position-information aiding andthe computed offset for the pairwise cross-correlation peakis useful to detect a meaconing attack.

Fig. 4: In our centralized spoofing detection algorithm, a net-work of receivers send the P(Y) snippet at a requested timeto the central unit, such as Phasor Data Concentrator (PDC).Later, the central unit collectively authenticates the spoofingstatus of all the receivers.

4. Centralized Platform for implementation:We implemented a centralized framework as shown in

Fig. 4 for our networked spoofing detection based on severalkey advantages. A centralized architecture allows for less

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computational complexity at each PMU as well as fewercommunication links in the network overall. In addition,given that currently the phasor data is authenticated at acentralized platform known as PDC, we will be able toeasily incorporate our algorithm in the existing power gridcommunication network.

III. NETWORKED SPOOFING DETECTION

In this section, we explain our networked spoofing de-tection algorithm in detail as shown in Fig. 5. Among thenetwork of receivers being evaluated, we assume to have atleast one common satellite in view for each receiver pair.

A. Conditioning of GPS signals

At the kth receiver, the baseband GPS signal [16] receivedfrom the ith satellite at time t is represented as

sik(t) = ΛiC(t)ci(t− τ̂ id)Di(t− τ̂ id) cos

(2πf̂ idt+ φiL1

)+ jΛi

P (t)pi(t− τ̂ id)Di(t− τ̂ id) sin(2πf̂ idt+ φiL1

) (1)

where Λic and Λi

p are the received signal power of the civilianC/A and military P(Y) components of the GPS L1 signal; Di

represents the navigation message with a bit rate of 50 bpsfor the ith satellite; ci is the C/A code sequence with achipping rate of fC/A = 1.023 MHz and pi is the P(Y)code sequence with a chipping rate of fP (Y ) = 10.23 MHz;φiL1 represents the carrier phase offset.

Fig. 5: Our networked GPS spoofing detection algorithmanalyzes the pair-wise cross-correlation of the conditionedGPS snippets obtained from a network of receivers.

Even though the encryption is unknown, different geo-graphically distributed receivers receive the same encryptedP(Y) codes and therefore a correlation peak across receiversis expected for the common satellites in view. By contin-uously tracking the GPS satellite signal, we compute anaccurate estimate of the GPS signal parameters, namely theC/A code delay τ̂ id,k and the carrier Doppler frequency f̂ id,k.After the tracking loops converge, at the receiver level,we perform carrier wipe-off of the incoming GPS signaland later isolate the corresponding quadrature component to

obtain the conditioned GPS snippet given by

siP,k[t] = I(sik[t] exp

(− j2πf̂d,kt− φiL1

))= Λi

P,kpik[t]Di

k[t] + nik[t](2)

where t ∈ {1, . . . , Tsnip} represents the indices of samplesconsidered with Tsnip = Lsnip/fC/A and nik[t] is the noiseassociated.

The length of the snippet Lsnip required for cross-correlation is determined by the narrow front-end samplingfrequency which attenuates the wide-band P(Y) signal. At anear-future receiver time requested by the central unit, theconditioned snippet mentioned in Eq. (2) is generated for allsatellites in view at each receiver. These snippets are sent tothe central unit for further processing.

B. Utilizing networked approach

Before performing pairwise cross-correlation we executetime alignment by utilizing various resources such ascounting the GPS C/A code chips, synchronizationinfrastructure of power grid using precise time protocolservices [17] and position-information aiding. Thereafter,we perform our networked spoofing detection analysis inthe central unit as follows:

1. Cross-correlating the receivers pairwise:The first step is to carry out pair-wise cross-correlations

among each pair of receivers for each common satellite inview. For each receiver pair (j, k), where j, k ∈ 1, ...,Mand j 6= k, and for the ith commonly viewed, the pair-wisecross-correlation is computed as:

ccij,k[t] =

Tsnip∑n=0

(siP,j [n+ t] ∗ conj(siP,k[n])

)(3)

where given M receivers,(M2

)pair-wise cross-correlations

are possible.

2. Evaluating the pair-wise spoofing decision:Next, we analyze the characteristics of the pair-wise cross-

correlation based on multiple hypothesis testing to detectthe spoofing attacks. We define three hypothesis based onthe characteristics of two test statistics i.e., pair-wise cross-correlation peak offset and magnitude:• H0 denotes the hypothesis that one or both the receivers

do not contain the P(Y) code sequence which is ob-served through the absence of peak in the pair-wisecross-correlation.

• H1 denotes the hypothesis that both the receiverscontain the exact same P(Y) code which is observedthrough a strong centered peak present in the pair-wisecross-correlation.

• H2 denotes the hypothesis that both the receivers con-tain P(Y) code but are shifted with respect to each other.This is observed through the presence of a strong off-centered peak in the pair-wise cross-correlation.

The foremost condition to be checked is the presence orabsence of a correlation peak above a certain pre-defined

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primary pair-wise threshold (max(ccik1,k2[t]

)> ζp). If

satisfied, the primary pair-wise statistic Bik1,k2

is votedone. If the condition is not satisfied, Bi

k1,k2is voted zero

in accordance with H0 hypothesis. This indicates theabsence of the authentic P(Y) code in one or both of thereceivers which may be due to the spoofing attacks, namelysignal-level spoofing.

3. Aggregating across satellites and receivers:In the third step, for each kth receiver, we aggregate

the primary pair-wise statistic obtained from the network ofreceivers and the corresponding common satellites in viewNn for each pair as

Bk =

M∑n=0,n6=k

Nn∑i=0

(wi

nBik,n

)(4)

where win = f

(SNRi

k, SNRin, cc

ik,n

)∗ g(Xk, Xn, S

i).Moreover, we perform weighted summation across satel-

lites and receivers wik to obtain a more accurate primary

cumulative statistic Bk. While summing across satellites,higher weight f(.) is given to the satellites with high pair-wise cross-correlation magnitude, elevation and Signal-to-Noise Ratio (SNR) as they experience lower signal blockageand atmospheric effects. Whereas in case of summationacross receivers, higher weight g(.) is given to the receiverpair with large enough separation distance so as to minimizethe cross-talk between channels [18].

TABLE I: Evaluating the cumulative pair-wise cross-correlation to obtain primary and secondary spoofing deci-sions for each receiver.

Cumulative Statistic Spoofing status ofkth receiverPrimary Secondary

Bk < ηp - Spoofed: absence of P(Y) codeBk ≥ ηp Ck ≥ ηs Meaconed: shifted P(Y) codeBk ≥ ηp Ck < ηs Not spoofed: centered P(Y) code

4. Evaluating the cumulative spoofing decision:If our primary cumulative statistic for a particular kth

receiver is less than a pre-defined primary cumulative thresh-old ηp i.e., Bk < ηp, then a spoofing decision of boolean trueis transmitted back to that receiver as seen in Table I.

However, if our statistic Bk ≥ ηp, then further analysis iscarried out to ascertain its spoofing status and verify that thereceiver is not experiencing a meaconing attack. To do this,we evaluate the expected offset between two receivers basedon position-information aiding using

∆tij,k = |∆tij −∆tik| (5)

where j ∈ {1, . . . ,M} ∀j 6= k.We evaluate another important metric from the pair-wise

correlation which is the peak offset i.e., argmax(ccij,k[t]

).

We evaluate if this following condition is satisfied i.e.,(|argmax

(ccij,k[t]

)− ∆tij,k|

)< ζs where ζs is the sec-

ondary pair-wise threshold. The violation of the above con-dition indicates the presence of a meaconing attack, since the

relative time delay between the signals received from the ith

satellite at the jth and kth receivers, is significantly greaterthan what is expected. Therefore, if the condition is true,the secondary pair-wise statistic Ci

j,k is voted one, therebysatisfying the H1 hypothesis. If found to be false, Ci

j,k = 0,satisfying H2 hypothesis.

Lastly, a cumulative summation is performed for each ofthe receivers as

Ck =

M∑n=0,n6=k

Nc,n∑i=0

(wi

nCik,n

)(6)

where Ck is the secondary cumulative statistic evaluated ateach receiver in the network i.e., k ∈ {1, . . . ,M}. Also, ηsis the pre-defined secondary cumulative threshold. Finally, ifCk ≥ ηs, then a boolean decision of the kth receiver beingauthentic is sent back to the receiver. However, if Ck < ηs,then a boolean decision of the kth receiver is being meaconedis sent back.

IV. EXPERIMENTS AND RESULTS

In this section, we demonstrate the successful detection ofseveral simulated spoofing attacks using our spoofing detec-tion algorithm, by performing collective time authenticationacross a geographically distributed network of receivers.

A. Experimental setup

Our experimental setup consists of four GPS stationslocated in Idaho, Illinois, Colorado and Ohio respectively asshown in Fig. 6. Each station is equipped with a Trimble Zplus antenna as seen in Fig. 7, external chip scale atomicclock Microsemi Quantum SA.45s CSAC and UniversalSoftware Radio Peripheral (USRP-N210) to collect the data.

Fig. 6: Four geographically distributed receivers are consid-ered at Mountain Home, Idaho; Boulder, Colorado; Cham-paign Illinois and Cleveland, Ohio.

We collected data at a sampling rate of 2.5 MHz with32-bit samples for Idaho and Illinois and 8-bit samples forColorado and Ohio respectively. We simulated the spoofingattacks and post-processed the collected raw GPS signalsusing our pyGNSS [19] platform, a Python based object-oriented framework.

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Fig. 7: Our data collection setup at one of our test sites.

We pre-compute the locations of the static receivers us-ing multi-receiver vector tracking [20] and use them forposition-information aiding. We generate the conditionedGPS snippets by performing scalar tracking and we evaluatethe robustness of our algorithm in detecting spoofing attacksfor this conventional GPS scalar tracking approach.

B. Intermediate results

In authentic conditions, the pair-wise cross-correlation ofthe time aligned conditioned GPS snippets between Illinoisand Colorado, 512 ms long is shown in Fig. 8. We observea strong single centered peak which indicates the presenceof the same P(Y) code sequence at both the receivers.

Fig. 8: Pair-wise cross-correlation peak of satellite PRN31 across Illinois and Colorado receivers. Centered cross-correlation peak indicates the presence of authentic P(Y)code sequence.

Fig. 9: Variation in PNR with increase in snippet length.For a snippet length of 128 ms, the PNR is 1.2 dB whichincreases to 2 dB for a snippet length of 512 ms.

We also conducted additional analysis to evaluate the trendof the Peak-to-Noise Ratio (PNR) by varying the snippetlength considered as seen in Fig. 9. Here, we can observethat by cross-correlating 128 ms snippet, the PNR is 1.2 dBwhich increases to 2 dB for a snippet length of 512 ms.

C. Under signal-level spoofing

We simulated signal-level spoofing by generating coun-terfeit signals that deviated from the authentic C/A codeat 0.01 ms/s. It is separately verified that under this sim-ulated spoofing attack, the traditional scalar tracking lockedonto the malicious signals and estimated inaccurate timethereby violating the IEEE C37.118.1 standards for PMUtiming accuracy [21].

These simulated spoofing attacks were added to the datacollected at Idaho receiver. We authenticated the Illinoisreceiver which is authentic and Idaho receiver which is beingspoofed by using a network of four cross-check receivers andsix pair-wise cross-correlations.

In Fig. 10, we observed that the value of primary cu-mulative statistic Bk of our Illinois receiver denoted bythe blue line, with average of 0.0156, is greater than thethreshold ηp = 0.003. However, for Idaho receiver denotedby the red line in the presence of spoofing, the valueof Bk < ηp and showed an average of 0.001. This is becausewhen the scalar tracking locked onto the malicious signals,which does not include the authentic P(Y) component andhence no cross-correlation peak as seen in Fig. 11. Therefore,we demonstrated successfully detection signal-level spoofingattacks using our networked spoofing detection algorithm.

Fig. 10: Variation in the primary cumulative statistic Bk. Thedotted black line denotes the primary cumulative thresholdηp = 0.003. The blue line represents the authentic Illinoisreceiver Bk > ηp while the red line represents the spoofedIdaho receiver Bk < ηp. Our algorithm successfully detectedthe occurrence of signal-level spoofing attack in Idaho.

D. Under meaconing

At the Idaho GPS receiver, we added simulated meaconingsignals of 3 dB higher power than the authentic signalsthat induced an error 0.5 ms in time. In this scenario, wecompared both the cumulative statistics i.e., primary Bk andsecondary Ck of meaconed Idaho receiver data and authenticIllinois data.

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Fig. 11: Pair-wise cross-correlation peak of conditioned GPSsignals obtained during simulated signal-level spoofing attackat Idaho receiver. No significant peak is found, indicating theabsence of P(Y) code sequence in Idaho receiver.

Fig. 12(a) showed that the primary cumulative statistic re-mained above the threshold ηp in both authentic and spoofedconditions. This is because the meaconing signals have theassociated P(Y) component and therefore a correlation peakwas still detected. However, in Fig. 12(b), we observed thatthe secondary cumulative statistic Ck which lies above thethreshold ηs = 0.15 for the authentic Illinois receiver, fellbelow the threshold for meaconed Idaho receiver, in thepresence of meaconing. This is due to the inconsistencyin the expected offset computed using position-informationaiding and the computed offset from the cross-correlation ofthe conditioned snippets.

(a) Bk (b) Ck

Fig. 12: Variation in the primary (Bk) and secondary (Ck)cumulative statistic. The black dotted line ηp = 0.003is the primary cumulative threshold whereas the magentadotted line ηs = 0.15 is the secondary threshold. Undermeaconing, the primary cumulative statistic is above theprimary threshold specified, but the secondary cumulativestatistic is below the secondary threshold thereby indicatinga meaconing attack.

V. CONCLUSIONS

We proposed our networked spoofing detection algorithmusing a network of receivers via pair-wise cross-correlationof the sampled military GPS signals without requiring theknowledge of the exact encrypted codes. We also utilizedposition-information aiding to compute the expected timeoffset between the received P(Y) codes at different GPSreceivers. Our experimental results validated the success-ful detection of signal-level spoofing and meaconing usingour networked approach. By incorporating our GPS timeauthentication algorithm as an additional protection layer,

we demonstrated an increased resilience in the GPS timingsupplied to the PMU devices.

ACKNOWLEDGMENT

This material is based upon work supported by theDepartment of Homeland Security under contract numberHSHQDC-17-C-B0025 and the applicable copyright noticeof 17 U.S.C. 401. In addition, the authors would like to thanktheir lab members at the University of Illinois: Arthur Chuand Cara Yang for helping with the experimental setup anddata collection.

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BIOGRAPHIES

Sriramya Bhamidipati is a doctoral student in theAerospace Engineering Department at the University ofIllinois at Urbana-Champaign. She received her M.S degreein Aerospace Engineering from University of Illinois atUrbana-Champaign in 2017. She received her B.Tech. withhonors in Aerospace Engineering and minor in Systems andControls Engineering from Indian Institute of TechnologyBombay, India in 2015.

Tara Yasmin Mina is a graduate student in thedepartment of Electrical and Computer Engineering at theUniversity of Illinois at Urbana-Champaign. She receivedher B.S. degree in Electrical Engineering from Iowa StateUniversity in 2017.

Grace Xingxin Gao received the B.S. degree inMechanical Engineering and the M.S. degree in electricalengineering from Tsinghua University, Beijing, China in2001 and 2003. She received the PhD degree in ElectricalEngineering from Stanford University in 2008. From 2008to 2012, she was a research associate at Stanford University.Since 2012, she has been with University of Illinois atUrbana-Champaign, where she is presently an assistantprofessor in the Aerospace Engineering Department.

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