Integrity Attacks on Real-Time Pricing in Smart Grids: Impact and Countermeasures

Giraldo, Jairo; Cárdenas, Álvaro A.; Quijano, Nicanor

doi:10.1109/tsg.2016.2521339

Cited by 97 publications

(40 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We solve the LMIs in [29] by letting c 3 = 0.500, M given as (18), H = I and minimizing µ to obtain observer with γ = 22.4. We let m ∼ U(−0.5, 0.5).…”

Section: Assumptionmentioning

confidence: 99%

A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks

Yang

Murguia

Kuijper

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

We address the problem of robust state estimation and attack isolation for a class of discrete-time nonlinear systems with positive-slope nonlinearities under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of circle-criterion observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose a estimator that provides robust estimates of the system state in spite of sensor attacks and measurement noise; and an algorithm for detecting and isolating sensor attacks. Our results make use of the ISS property of the observers to check whether the trajectories of observers are consistent with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the results.

show abstract

“…We solve the LMIs in [29] by letting c 3 = 0.500, M given as (18), H = I and minimizing µ to obtain observer with γ = 22.4. We let m ∼ U(−0.5, 0.5).…”

Section: Assumptionmentioning

confidence: 99%

A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks

Yang

Murguia

Kuijper

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

show abstract

“…For given false alarm rate A and probability p ∈ (0, 1), the p-probable hidden reachable set of the attack sequence δ k in (14), R p α , is defined as the set of e k ∈ R n , k ∈ N that can be reached from the origin e 1 = 0 due to the the attacker's action δ k restricted to satisfyĀ = A and…”

Section: A Attack Model and Hidden Reachable Setsmentioning

confidence: 99%

On Reachable Sets of Hidden CPS Sensor Attacks

Murguia

Ruths

2018

2018 Annual American Control Conference (ACC)

View full text Add to dashboard Cite

For given system dynamics, observer structure, and observer-based fault/attack detection procedure, we provide mathematical tools -in terms of Linear Matrix Inequalities (LMIs) -for computing outer ellipsoidal bounds on the set of estimation errors that attacks can induce while maintaining the alarm rate of the detector equal to its attack-free false alarm rate. We refer to these sets to as hidden reachable sets. The obtained ellipsoidal bounds on hidden reachable sets quantify the attacker's potential impact when it is constrained to stay hidden from the detector. We provide tools for minimizing the volume of these ellipsoidal bounds (minimizing thus the reachable sets) by redesigning the observer gains. Simulation results are presented to illustrate the performance of our tools.

show abstract

“…The authors used Kalman filter based approach to resolve the synchronization problem in phase measurement units while using large scale deployment. The authors of [40] proposed a system that can generate any arbitrary pricing signal. The proposed system is able to detect the correct pricing signal and protect any attack against pricing.…”

Section: Recent Advances Of the Signal Processing Techniques In Smartmentioning

confidence: 99%

Recent advances of the signal processing techniques in future smart grids

Uddin

Ahmad

Qamar

et al. 2018

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

IntroductionSmart grid is a network of electric supply that manages power demand in reliable and economic manner by detecting and reacting to local changes in usage. The infrastructure comprises of smart meters, appliances, and resources with a combination of modern technologies like, control, power, instrumentation, and communication. In such a complex scenario, signal processing techniques are essential to understand, plan, design and operate the complex future smart electronic grids [1]. In addition to this, signal processing has wide variety of applications and is becoming an important tool for electric power system analysis. This is due to the fact that measurements retrieved from numerous locations of the grid can be used for data analysis. These measurements can also be used for a variety of issues such as voltage control, power quality and reliability, power system and equipment diagnostics, power system control and protection, etc [2][3][4][5][6].Power quality is one of the main issue of the smart grid research where voltage, current and frequency deviations in the power system are the main concerns of the system operator [7]. The characterization of the incompatibilities caused by these deviations requires an understanding of their principal cause. Other possible aspects that need inspection are the efficient representation of the voltage and current variations in various electrical Abstract Smart grid is an emerging research field of the current decade. The distinguished features of the smart grid are monitoring capability with data integration, advanced analysis to support system control, enhanced power security and effective communication to meet the power demand. Efficient energy consumption and minimum costs are also included in the prodigious features of smart grid. The smart grid implementation requires intelligent interaction between the power generating and consuming devices that can be achieved by installing devices capable of processing data and communicating it to various parts of the grid. The efficiency of these devices is greatly dependent on the selection and implementation of the advance digital signal processing techniques. This paper provides a comprehensive survey on the applications of signal processing techniques in smart grids, plus the challenges and shortcomings of these techniques. Furthermore, this paper also outlines some future research directions related to applications of signal processing in smart grids. Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) Page 2 of 15 Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) 8:2 equipment. Moreover, the signal processing of the power patterns leads to better understanding the behavior of these equipment. Continuous monitoring is also required to capture various events and variations. To meet future demands, methods and techniques must be developed to explore the full range of signals derived from the complex interaction between suppliers, consumers and network operators [8]. REVIEWA smart grid performs measurement, monitoring ...

show abstract

Integrity Attacks on Real-Time Pricing in Smart Grids: Impact and Countermeasures

Cited by 97 publications

References 18 publications

A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks

A Robust Circle-criterion Observer-based Estimator for Discrete-time Nonlinear Systems in the Presence of Sensor Attacks

On Reachable Sets of Hidden CPS Sensor Attacks

Recent advances of the signal processing techniques in future smart grids

Contact Info

Product

Resources

About