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Protecting Our Future Power Grid: Defending Against False Data Injection Attacks

Protecting Our Future Power Grid: Defending Against False Data Injection Attacks. Adam Magaña & Jerald Dawson. Graduate Mentor: Saurabh Amin. Research Supervisor: Alvaro C á rdenas & Annarita Giani. Faculty Mentor: Prof. S. Shankar Sastry. Gravity of the Situation….

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Protecting Our Future Power Grid: Defending Against False Data Injection Attacks

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  1. Protecting Our Future Power Grid:Defending Against False DataInjection Attacks Adam Magaña & Jerald Dawson Graduate Mentor: SaurabhAmin Research Supervisor: Alvaro Cárdenas & AnnaritaGiani Faculty Mentor: Prof. S. Shankar Sastry

  2. Gravity of the Situation… • “Any failure of our electrical grid, whether intentional or unintentional, would have a significant and potentially devastating impact on our nation.” –Rep. Bennie Thompson, MS • A good example of a bad situation • 2003 Northeast Blackout • August 14–18, 2003 • 55,000,000 people effected • 13 major cities lost power

  3. Terminology… • Power Grid- An interconnected network for delivering electricity from suppliers to consumers. • SCADA- Supervisory Control and Data Acquisition (data aggregation system) • State Estimation- A Process of estimating unknown state variables in a power grid based on meter measurements. • False Data Injection- A data corruption method that involves a hacker replacing good data with bad data for a given sensor.

  4. Network Model… Human Control State Estimation Attacks! Supervisory Control and Data Acquisition Attacks!

  5. False Data Injection Detection… • Hacker Intercepts Data • H(x) is measurement, e is measurement error, a is attack • Additive Attack: • ź = [H(x) + e] + a • Scale Attack: • ź = [H(x) + e] * a • Numerous types of attacks • Goes completely unnoticed if network security fails • Another way to detect bad data?

  6. Detecting Bad Data… Actual Measurement Estimated Measurement Measurement Residual ? Sum of Squares of Measurement Residual > Bad Data Threshold If data falls outside of the threshold, it is classified as bad data and is removed from the estimate.

  7. Simulating Attacks… • Work based off NCSU paper, CCS ‘09 • Matlab, Matpower Simulation Package • State Estimation Simulator • Modified for 14-bus system • Custom controller script • Can iterate state estimator thousands of times with random measurements attacked • Allows for large amounts of trial • Large iterations required due to sensor error • Requires the user only run one command

  8. Traversing the Algorithm… Loops and logs data… …until iterations are complete.

  9. Terminal Output… • se_repeat(iterations, attack-index, perturbation) • se_repeat(10,1,10)

  10. Graphical Output… • se_repeat(iterations, attack-index, perturbation) • se_repeat(1000,1,10)

  11. What did I learn? • My initial hypothesis was correct • Higher corruption = higher attack detection • Poses strong requirements for attackers • Successful attacks require high coordination, minimal singular influence • Possible Future research • Changes in bad data threshold • Alternate detection algorithms • May not require topology and parameter information

  12. Test for Detecting Bad Data in WLS State Estimation • Solve WLS estimation problem and compute objective function. • Where: • is the estimated state vector of dimension n. • is the estimated measurement i. • Zi is measured value of the measurement i. • = Rii is the variance of the error in measurement i. • m is number of measurements.

  13. Chi-square • Look up the value from the Chi-squares distribution table corresponding to a detection confidence with probability p and (m-n) degrees of freedom. Let this value be x2(m-n),p. • Here p= Pr( ≤x2(m-n),p). • Test if ( Pr≤x2(m-n),p). If yes then bad data will be suspected, if not the measurements will be free of bad data.

  14. Findings I ran 73 different simulations of an attack using matpower via matlab. According to my findings there 40 out of 73 attempts were bad data, 30 out of 73 attempts had no rejection and 3 out of 73 had two indexes rejected in one iteration.

  15. Identification of bad data err(x) = err(x)*10 [this creates a bad measurement]

  16. Results

  17. Results cont.

  18. What did I learn? • Learned about state estimation • How false data is injected • Chi-square testing • For future research, more simulations can be run and a better program can be found to run the simulators more accurately and for more precise data.

  19. Any Questions?

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