Integrity Data Attacks in Power Market Operations

By Le Xie, Yilin Mo, Bruno Sinopoli. IEEE Transactions on Smart Grid, 2011

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e366670f1c434d9f692e7723735977c2a0f4525f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Junhua Zhao
2. Ling Shi
3. Zhao Yang Dong
4. Rongxing Lu
5. Zhu Han
6. Mohammad Pirani
7. Guo Chen
8. Gaoqi Liang

In Computer Networks and Communications

1. Zhao Yang Dong
2. Ling Shi
3. Junhua Zhao
4. Gaoxi Xiao
5. Rongxing Lu
6. Zhu Han
7. Guo Chen
8. Gaoqi Liang

In Control and Systems Engineering

1. Zhao Yang Dong
2. Ling Shi
3. Anuradha M. Annaswamy
4. Mohammad Pirani
5. Aranya Chakrabortty
6. Seyed Mehran Dibaji
7. David Bezalel Flamholz
8. Dawei Shi

Citations in the corpus

• A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, background): “Authors of [30], [31] introduced a detailed investigation of the effect of FDIA on the energy market process specifically the locational marginal prices (LMP).”
• Distributed Blockchain-Based Data Protection Framework for Modern Power Systems Against Cyber Attacks (2019, background): “Many studies [9]–[13] have demonstrated the impacts of FDIAs on modern power systems.”
• Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “Along with affecting the state estimations, FDIA can disrupt electricity markets through false economic dispatch and data [196], [198], [199].”
• A systems and control perspective of CPS security (2019, background): “3) Market level: False data injection in electricity markets has been investigated in [52], where a convex optimization problem is solved by the attacker to find which nodal price of Ex-Post market must be manipulated to maximize the financial profit of the attacker.”
• Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid (2017, background): “The works in [8] and [9] analyze the economical effect of false data injection on the transmission line congestion.”
• False Data Injection on State Estimation in Power Systems—Attacks, Impacts, and Defense: A Survey (2017, background): “[36], [37] show that the adversary can launch FDI attacks for continuous financial arbitrage.”
• Time Synchronization Attack in Smart Grid: Impact and Analysis (2013, background): “Meanwhile, malicious attack against measurement data, namely false data injection attack (FDIA) has been studied in [14], [18], [26].”
• A Review of False Data Injection Attacks Against Modern Power Systems (2017, methodology): “References [26] and [27] develop a heuristic-based method for the attacker to improve the attack efficiency.”
• Optimal Linear Cyber-Attack on Remote State Estimation (2017, background): “Furthermore, the consequence of the false-data injection attack and the reachable state estimation error have been analyzed in [21].”