bad data injection in smart grid: attack and defense mechanisms zhu han university of houston
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Bad Data Injection in Smart Grid: Attack and Defense Mechanisms
Zhu HanUniversity of Houston
OverviewOverview
Introduction to Smart Grid
Power System State Estimation Model
Bad Data Injection
Defender Mechanism – Quickest Detection
Attacker Learning Scheme– Independent Component Analysis
Future Work
A Few Topics in Smart Grid Communication
Conclusions
Quick View of Amigo Lab
““Smarter” Power GridSmarter” Power Grid Sensing, measurement, and control devices with two-way
communications between the suppliers and customers.
Benefits both utilities, consumers & environment:– Reduce supply while fitting demand
– Save money, optimal usage.
– Improve reliability and efficiency of grid
– Integration of green energy, reduction of CO2
More than 3.4 billion from US federal stimulus bill is targeted.– Obama stimulus plan
One of hottest topic in research community– But what are the problems from signal processing, communication
and networking points of view?
Smart GridSmart GridAre more easily integrated into power sys. Less
depend on fossil fuel
Are more easily integrated into power sys. Less
depend on fossil fuel
Connect grid to charge overnight when demand is
low
Connect grid to charge overnight when demand is
low
Realtime analysis, Manage, plan, and forecast the energy system to meets the
needs
Realtime analysis, Manage, plan, and forecast the energy system to meets the
needs
Can generate own and sellback excess
energy
Can generate own and sellback excess
energy
Gather, monitor the usage so the supply more efficiently and
anticipate challenging peaks
Gather, monitor the usage so the supply more efficiently and
anticipate challenging peaks
Use sophisticated comm. Technology to find/fix problems
faster, enhancing reliability
Use sophisticated comm. Technology to find/fix problems
faster, enhancing reliability
in-home management tool to track usage
in-home management tool to track usage
Supervisory Control and Data Acquisition CenterSupervisory Control and Data Acquisition Center
Real-time data acquisition– Noisy analog measurements
Voltage, current, power flow– Digital measurements
State estimation– Maintain system in normal
state
– Fault detection
– Power flow optimization
– Supply vs. demand
SCADA TX data from/to Remote Terminal Units (RTUs), the
substations in the grid
SCADA TX data from/to Remote Terminal Units (RTUs), the
substations in the grid
Privacy & Security Concern Privacy & Security Concern
More connections, more technology are linked to the obsolete infrastructure.
– Add-on network technology: sensors and controls estimation
– More substations are automated/unmanned
Vulnerable to manipulate by third party– Purposely blackout
– Financial gain
– Story of Enron
How to tackle this issue at this moment?Provide one example
next
Power System State Estimation ModelPower System State Estimation Model
Transmitted active power from bus i to bus j– High reactance over resistance ratio
– Linear approximation for small variance
– State vector , measure noise e with covariance Ʃe
– Actual power flow measurement for m active power-flow branches
– Define the Jacobian matrix
– We have the linear approximation
– H is known to the power system but not known to the attackers
Bad Data Injection and Detection Bad Data Injection and Detection
State estimation from z
Bad data detection– Residual vector
– Without attacker
where
– Bad data detection (with threshold )
without attacker:
with attacker: otherwise
Stealth (unobservable) attack: z=Hx+c+e, where c=Hx
– Hypothesis test would fail in detecting the attacker, since the control center believes that the true state is x + x.
OverviewOverview
Introduction to Smart Grid
Power System State Estimation Model
Bad Data Injection
Defender Mechanism – Quickest Detection
Attacker Learning Scheme– Independent Component Analysis
Future Work
A Few Topics in Smart Grid Communication
Conclusions
Quick View of Amigo Lab
Basics of Quickest Detection (QD)Basics of Quickest Detection (QD)
Detect distribution changes of a sequence of observations as quick as possible with the constraint of false alarm or detection probability.
min [processing time]
s.t. Prob(true ≠ estimated) < ŋ Classification
1. Bayesian framework: known prior information on probability SPRT (e.g. quality control, drug test, )
2. Non-Bayesian framework: unknown distribution and no prior CUSUM (e.g. spectrum sensing, abnormal detection )
QD System Model QD System Model
Assuming Bayesian framework with non-stealthy attack– the state variables are random with
The binary hypothesis test:
The distribution of measurement z under binary hyp: (differ only in mean)
We want a detector– False alarm and detection probabilities
Detection Model - NonBayesianDetection Model - NonBayesian Non-Bayesian approach
– unknown prior probability, attacker statistic model
The unknown parameter exists – in the post-change distribution and may changes over the
detection process.
– You do not know how attacker attacks.
Minimizing the worst-case effect via detection delay:
We want to detect the intruder as soon as possible while maintaining PD.
Actual time of active attack
Actual time of active attack
Detection time
Detection time
Detection delay
Detection delay
Multi-thread CUSUM AlgorithmMulti-thread CUSUM Algorithm
CUSUM Statistic:
where Likelihood ratio term of m measurements:
By recursion, CUSUM Statistic St at time t:
Average run length (ARL) for declaring attack with threshold h
How about the unknown?
How about the unknown?
Declare the attacker is existing!
Otherwise, continuous to the process.
Linear Solver for the UnknownLinear Solver for the Unknown
Rao test – asymptotically equivalent model of GLRT:
The linear unknown solver for m measurements:
Recursive CUSUM Statistic w/ linear unknown parameter solve:– Modified CUSUM statistics The unknown is no long
involvedThe unknown is no long
involved
Simulation: Adaptive CUSUM algorithmSimulation: Adaptive CUSUM algorithm
2 different detection tests: FAR: 1% and 0.1%
Active attack starts at time 5
Detection of attack at time 7 and 8, for different FARs
Markov Chain based Analytical ModelMarkov Chain based Analytical Model
Divide statistic space into discrete states between 0 and threshold– Obtain the transition probabilities
– Obtain expectation of detection delay, false alarm rate and missing probability
OverviewOverview
Introduction to Smart Grid
Power System State Estimation Model
Bad Data Injection
Defender Mechanism – Quickest Detection
Attacker Learning Scheme– Independent Component Analysis
Future Work
A Few Topics in Smart Grid Communication
Conclusions
Quick View of Amigo Lab
Independent Component Analysis (ICA)Independent Component Analysis (ICA)
Linear Independent Component Analysis– find a linear representation of the data so that components are
as statistically independent as possible.
– i.e., among the data, find how many independent sources.
Question for bad data injection:– Without knowing H, the attacker can be caught.
– Could attacker launch stealthy attack to the system even without knowledge about H?
– Using ICA, attacker could estimate H and consequently, lunch an undetectable attack.
ICA BasicsICA Basics
A special case of blind source separation
u = G v
u = [ui, i = 1, 2, … m]: observable vector
G = [gij, i = 1, 2, … m, j = 1, 2, … n]: mixing matrix
(unknown)
v = [vi, i = 1, 2, … n]: source vector (unknown)
Linear ICA implementation: FastICA from [Hyvärinen]
Stealth False Data Injection with ICAStealth False Data Injection with ICA
Supposing that the noise is small, then we what to do the
mapping:
u = G v z = H x
Problem: state vector x is highly correlated
Consider: x = A y, where– A: constant matrix that can be estimated
– y: independent random vectors
Then we can apply Linear ICA on z = HA y
– We cannot know H, but we can know HA
– Stealthy attack: Z=Hx+HAy+e
Numerical Simulation SettingNumerical Simulation Setting Simulation setup
– 4-Bus test system, IEEE 14-Bus and 30-bus
– Matpower
Numerical Results Numerical Results MSE of ICA inference (z-Gy) vs. the number of observations
(14-bus case).
Performance of the AttackPerformance of the Attack
The PDF is the same w or w/o attacking. So log likelihood is equal to 1– unable to detect
OverviewOverview
Introduction to Smart Grid
Power System State Estimation Model
Bad Data Injection
Defender Mechanism – Quickest Detection
Attacker Learning Scheme– Independent Component Analysis
Future Work
A Few Topics in Smart Grid Communication
Conclusions
Quick View of Amigo Lab
1. Distributed Smart Grid State Estimation 1. Distributed Smart Grid State Estimation
The deregulation has led to the creation of many regional transmission organizations within a large interconnected power system.
A distributed estimation and control is need .– Distributed observability analysis
– Bad data detection
Challenges:– Bottleneck and reliability problems with one coordination center.
– Need for wide area monitoring and control
– Convergence and optimality
Fully-Distributed State EstimationFully-Distributed State Estimation
With N substations/nodes
– By iteratively exchanging information with neighbors
– All local control center can achieve an unbiased consensus of system-wide state estimation.
Local observation matrix
Unknown State
Local Jacobian matrix
Useful information to be detected
2. Optimality of Fault Detection Algorithm 2. Optimality of Fault Detection Algorithm
Detecting the attack as an intermediate step towards obtaining a reliable estimate about the injected false data– Facilitates eliminating the disruptive effects of the false data
Joint estimation and detection problem– Define an estimation performance measure
– Seek to the optimize it while ensuring satisfactory of the detection performance
Performance measurement
3. Manipulate Electricity Market 3. Manipulate Electricity Market
Example: Ex Post MarketMarket that recalculate optimal points for generation and
consumption based on real-time data
Min :
St:
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iiii PgPgC
1
* )(
LlFFF
IiPgPgPg
PPg
lll
iii
I
iL
I
ii
,...,1
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maxmin
11
[28]
Generation Cost
Power Balance
Generation & Transmission limits
4. PMU4. PMU
PMU can measure voltage angle directly– Defender: placement problem, no need to place nearby
– Attackers’ new strategy with existence of PMU
1
6
2
5
7
3
4
PMU PMUPMU
PMU
PMUPMU
PMU
[29]
5. Game Theory Analysis5. Game Theory Analysis
(attacker,defender)
N A
N (0,0) (b,-b)
D (c,-c) (-a,a)
a, b, c
t
How to formulate the game?
A Few Topics in Smart Grid CommunicationsA Few Topics in Smart Grid Communications
Bad data injection
Demand side management– Peak to average ratio
– Scheduling problem
Renewable energy– The renewable energy is unreliable.
– Have to use diesel generators during shortage
– Not cheap and not green
PHEV – routing, scheduling and resource allocation
Communication link effect on the smart grid
ConclusionsConclusions
Bad data injection problem formulation From defender point of view
– detect malicious bad data injection attack as quick as possible – Adaptive CUSUM algorithm
From attacker point of view– can estimate both the system topology and power states just by
observing the power flow measurements– Independent component analysis algorithm to obtain information– Once the information is at hand, malicious attacks can be launched
without triggering the detection system Many possible future work Edited book 2012 by Cambridge with E. Hossain and V. Poor. Possible future collaboration
Overview of Wireless Amigo LabOverview of Wireless Amigo Lab Lab Overview
– 7 Ph.D. students, 2 joint postdocs (with Rice and Princeton)
– supported by 5 NSF,1 DoD, and 1 Qatar grants
Current Concentration– Game theoretical approach for wireless networking
– Compressive sensing and its application
– Smartgrid communication
– Bayesian nonparametric learning
– Security: trust management, belief network, gossip based Kalman
– Physical layer security
– Quickest detection
– Cognitive radio routing/security
– Sniffing: femto cell and cloud computing
USRP2 Implementation Testbed
QuestionsQuestions
Thank you for listening and supporting!
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