Detecting GPS Spoofing via a Multi-Receiver Hybrid ... monitoring of power grid through a widely...

University of Illinois at Urbana-Champaign 0

Detecting GPS Spoofing via a Multi-Receiver

Hybrid Communication Network for Power Grids

Tara Mina, Sriramya Bhamidipati, and Grace Xingxin Gao

University of Illinois at Urbana-Champaign

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Goals for Power Grid Modernization

• Automatic control of power grid

• Reduce failures or large-scale

blackouts (Ex: NE Blackout 2003)

• Improve visualization of power flow

• Continuously monitor state of

U.S. power grid network

• Install robust network of monitoring devices across the grid


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Synchronizing Data in Power Grid Network

Real-time monitoring of power grid through a widely dispersed

network of Phasor Measurement Units (PMUs)

− PMUs measure voltage and current phasors

− Provides measurement with precise time-stamp, via GPS

− Significant timing inaccuracies can induce a generator to trip [1]

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GPS used for synchronization of

PMU measurements

Power grid

PMUGPS clockGPS

Antenna

[1] Shepard, et al, GPS World, 2012


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Global Positioning System (GPS)

• Number of satellites: 31 operational

• Orbit: ≈ 20,200 𝑘𝑚 in altitude ( ≈ 12 ℎ𝑟 period orbit )

• Each satellite:

− Carries several atomic clocks (Cesium and/or Rubidium)

− Continuously sends precisely timed signals to Earth

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Block IIF

Satellite

(Boeing)


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How GPS Enables Navigation

• Precise satellite position (𝑿𝑺, 𝒀𝑺, 𝒁𝑺) provided to user

• After receiver obtains the satellite signal:

− Deciphers exact time of transmission 𝒕𝑻𝑿 of received signal

− Notes user’s received time 𝒕𝑹𝑿, and compares to

compute distance from the satellite

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But, user’s clock is not accurate…


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4

But, user’s clock is not accurate…

→ 𝑡𝑅𝑋 is inaccurate


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roughly approximate distance from the satellite

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“Pseudo” because:

Receiver clock is inaccurate

→ 𝑡𝑅𝑋 is inaccurate

→ 𝑐 𝑡𝑅𝑋 − 𝑡𝑇𝑋 ≠ 𝑑 (true range)

computed pseudorange 𝝆


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4

receiver clock bias correction


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• User has 4 unknowns:

− 3D Position 𝑿𝑹, 𝒀𝑹, 𝒁𝑹− Clock bias 𝚫𝒕

• Require at least 4 equations,

or satellites in view(usually ≥ 6 in open environments)

• For each satellite signal, we

have 1 equation:

𝜌 = 𝑐(𝑡𝑅𝑋 − 𝑡𝑇𝑋) = 𝑑 − 𝑐 Δ𝑡

= 𝑋𝑆 − 𝑿𝑹2 + 𝑌𝑆 − 𝒀𝑹

2 + 𝑍𝑆 − 𝒁𝑹2 − 𝑐 𝚫𝒕


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Civilian GPS and its Vulnerability

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• Commercial (non-military) users utilize civilian GPS signal

• Civilian GPS signal (C/A) in L1 band:

− Center frequency: 1575.42 MHz

− Bandwidth: 2.046 MHz

− Available to all users


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Military Signals for Authentication

Encrypted Military P(Y) GPS signal

− Orthogonal to civilian GPS signals, with same center frequency

− Because of encryption, cannot be generated by spoofer

− Presence of P(Y) signal in quadrature phase component

indicates authentic GPS signal [2-3]

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[2] Lo, et al, Inside GPS, 2009

[3] Psiaki, et al, ION GNSS, 2011[3]


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Prior Work and Main Challenges

• Shown handful of receivers (2-8) can be authenticated [4]

• Utilized centralized framework approach [5]

• Must extend to entire widespread network of PMUs

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[4] Heng, Work & Gao, IEEE ITS, 2015

[5] Bhamidipati, Mina & Gao, ION PLANS, 2018

[6] Hazra, et al, IEEE PES ISGT, 2014[6]


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Key Objectives

• Develop spoofing detection architecture for coordinated

authentication of all PMUs, with existing resources

• Provide defense against coordinated spoofing attacks

• Demonstrate successful operation of algorithm during

government-sponsored, real-world spoofing scenario

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Outline• GPS: How it Works

• Hybrid Network Architecture Framework

• Spoofing Detection Approach

− Pairwise Check and Preliminary Statistic Computation

− Regionally Representative Snippet

• Implementational Considerations

− Communication Protocol

− Spoofing Risk Assessment

− Subset Selection Algorithm

• Experimental Setup and Results

• Summary

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16

NASPInet Communication Structure

• North American

Synchrophasor

Initiative network

(NASPInet) [9]

• Regional utility

networks connected

via Data Bus

• Resources

prioritized in regional

sub-networks

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[9] Hu, Yi, NASPInet Technical Specifications, U.S. DOE, 2009


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Hierarchical Architecture Network

• Utilize communication to compare received GPS signals

• Proposed hybrid architecture network will overlay NASPInet

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High-level Process Diagram

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Outline

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• GPS: How it Works










• Summary


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Typical Correlation Observed (Authentic)

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Typical correlation (authentic): single peak above noise floor


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Typical Correlation Observed (Spoofed)

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Typical correlation (spoofed): no peak above noise floor


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Pairwise Statistic for Cross-Checking

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• Correlation result 𝑃𝑟𝑖𝑟𝑗,𝑘 between receivers 𝑟𝑖 and 𝑟𝑗 for PRN 𝑘:

− Authentic: 𝑃𝑟𝑖𝑟𝑗,𝑘 ∼ 𝑝0 = 𝒩 𝜇, 𝜎2 where 𝜇 > 0

− Spoofed: 𝑃𝑟𝑖𝑟𝑗,𝑘 ∼ 𝑝1 = 𝒩 0, 𝜎2

• Pairwise statistic 𝛾𝑟𝑖𝑟𝑗,𝑘 :

− Indicates amount of signal match for PRN 𝑘 between receivers 𝑟𝑖 and 𝑟𝑗− Consists of 2 terms:

○ Thresholded correlation result: 𝑃𝑟𝑖𝑟𝑗,𝑘𝑇 = 𝑃𝑟𝑖𝑟𝑗,𝑘𝟙 𝑃𝑟𝑖𝑟𝑗,𝑘 ≥ 𝜏𝑝𝑎𝑖𝑟

○ Pairwise weight 𝑤𝑟𝑖𝑟𝑗,𝑘, accounts for signal quality, receiver reliability, etc.

𝛾𝑟𝑖𝑟𝑗,𝑘 = 𝑤𝑟𝑖𝑟𝑗,𝑘 𝑃𝑟𝑖𝑟𝑗,𝑘𝑇


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Authentication within Regional Network


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Incorporate Representative Snippets

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Outline

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• Summary


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Data Required for Communication Protocol

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Data items to be sent by each PMU:

− Raw GPS signal fragment

− Signal tracking parameters for each visible satellite PRN

○ Time of transmission start index

○ Doppler Frequency

○ Carrier phase


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Communication Protocol Structure

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• Data block: data for

each authentication

time

• Data Packet: ~1 KB

of specific data with

header information

• Data Frame:

organizes data into

segments, includes

check sum

Segmented data structure allows for:➢ Isolation of corrupted/missing data ➢ Optimized rate of data transfer and storage


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Bandwidth Requirements

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• Reducing communication bandwidth requirements:

− Raw GPS signal fragment sent from PMU devices to PDC

− Appropriate signal tracking parameters sent for processing

• Main factors affecting overall bandwidth:

− Signal fragment length (500 milliseconds)

− Sampling rate (2.5 MHz)

− Data sample resolution (8-bit samples)

− Tracking parameter resolution (32-bit samples)

− Number of visible satellite PRNs (about 6)

− Desired rate of authentication (assuming 1 per minute)

• Bandwidth computed: ~23 KB per second

• Fiber optic cable: ~10 GB per second ( < 0.001% bandwidth)


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Evaluation of Spoofing Risk

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Historical

data

Pseudorange

residuals

SNR

values

Clock

residuals

Known

position

Bernoulli

distribution

Local

oscillator

Chi-squared

distribution

Empirical

distribution

Weighted

average

Spoofing risk

𝑝 𝑟𝑡 𝑟𝑡−1:𝑡−𝑊𝑝 𝑟𝑡 𝑆𝑁𝑅1:𝑁

𝑝 𝑟𝑡 Δ𝜌1:𝑁 𝑝 𝑟𝑡 Δ𝑇

𝑝(𝑟𝑡)


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Optimization: Subset Selection

• For cross-checking:

− Utilizing all PMUs, quite

computationally expensive

− Optimal subset of PMUs

• Cost function:

𝑓 Ω =

𝑖,𝑗 ∈ Ω; i≠j

𝑔 𝑖 𝑔(𝑗)ℎ(𝑖, 𝑗)

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• 𝑔 𝑖 = 1 − spoofing risk ∗ comm. link ∗ security

• ℎ 𝑖, 𝑗 = 𝑑𝑖𝑠𝑡(𝑖, 𝑗): Larger the separation, lesser

likelihood of both spoofed


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Outline

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• Summary


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Experimental Setup

Recorded GPS signal during live-sky spoofing event

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Sample rate: 2.5 𝑀𝐻𝑧

Snippet length: 500 𝑚𝑠

Post-process: PyGNSS [10]

Spoofing Data

Collection Setup

Rooftop

Antenna

Setup

[10] Wycoff & Gao, GPS World, 2015


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Preliminary Threshold Determination

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Threshold chosen to maximize authentic / spoofed conditional probabilities

Authentic:

𝛼 = 27.2𝑐 = 0.517𝛽 = 1.82𝑙 = 486

Spoofed:

𝛼 = 11.3𝑐 = 0.370𝛽 = 0.346𝑙 = 0

Generalized Gamma pdf:

𝑓 𝑥, 𝛼, 𝑐, 𝛽, 𝑙 =𝑐 𝑦𝑐𝛼−1exp(−𝑦𝑐)

𝛾(𝛼)

𝑦 = 𝛽(𝑥 − 𝑙)


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Preliminary Statistics – Regional Networks

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Spoofed

Authentic

Threshold

Threshold Authentic


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Secondary Threshold Determination

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Threshold chosen to maximize authentic / spoofed conditional probabilities

Authentic:

𝛼 = 1.53𝑐 = 1.74𝛽 = 33.7𝑙 = 20.0

Spoofed:

𝛼 = 1.18𝑐 = 2.69𝛽 = 5.80𝑙 = 13.7

Generalized Gamma pdf:

𝑓 𝑥, 𝛼, 𝑐, 𝛽, 𝑙 =𝑐 𝑦𝑐𝛼−1exp(−𝑦𝑐)

𝛾(𝛼)

𝑦 = 𝛽(𝑥 − 𝑙)


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Final Statistic – Representative Snippets

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• U.S. representative snippet matches that of South America

• Snippet at Western U.S. receiver (spoofed) has poor match

ThresholdSpoofed

Signal from

Authentic

Receivers


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Summary

• Proposed hybrid architecture to detect spoofing at each PMU

− Provides a defense against coordinated attacks on regional networks

− Uses regionally representative snippets to reduce bandwidth/processing

• Demonstrated algorithm successfully operates on wide-spread

network during government-sponsored, real-world spoofing attack

− Detects signal manipulation on victim receiver

− Simultaneously authenticates other receivers in hybrid network

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Acknowledgements

Special thanks to:

Prof. Jade Morton and Mr. Steve Taylor

for collecting data at the Peru, Chile, Colorado, and Ohio sites.

Additionally, thanks to our lab members:

Craig Babiarz, Arthur Chu, Matthew Peretic, and Cara Yang

for assisting with the experimental setup and data collection at the

Illinois site and the Western U.S. spoofing location.

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Thank You!

Tara Yasmin Mina

Electrical and Computer Engineering

Email: [email protected]

Sriramya Bhamidipati

Aerospace Engineering

Email: [email protected]

mailto:[email protected]

mailto:[email protected]

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