Model-Based Attack Detection and Mitigation for Automatic Generation Control

By Siddharth Sridhar, M. Govindarasu. IEEE Transactions on Smart Grid, 2014

441 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/05b4786918d078b1e24ba3570d614d51afda92e1

How researchers see this paper

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

In Computer Networks and Communications

1. Rajaa Vikhram Yohanandhan
2. Rajvikram Madurai Elavarasan
3. Lucian Mihet-Popa
4. Premkumar Manoharan
5. Zhao Yang Dong
6. Guo Chen
7. Alvaro A. Cárdenas
8. Henrik Sandberg

In Control and Systems Engineering

1. Henrik Sandberg
2. Alvaro A. Cárdenas
3. Rajaa Vikhram Yohanandhan
4. Rajvikram Madurai Elavarasan
5. Lucian Mihet-Popa
6. Premkumar Manoharan
7. Zhao Yang Dong
8. Jairo Alonso Giraldo

In Electrical and Electronic Engineering

1. Rajaa Vikhram Yohanandhan
2. Rajvikram Madurai Elavarasan
3. Lucian Mihet-Popa
4. Premkumar Manoharan
5. Zhao Yang Dong
6. Guo Chen
7. Henrik Sandberg
8. Nils Ole Tippenhauer

Citations in the corpus

• Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications (2020, methodology): “The analytical model can also be built to analyze the tampered data on power system measurements on voltage stability, Automatic Generation Control (AGC), and power system frequency control [120], [121].”
• A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, methodology): “In AGC systems, LF [84] illustrates a better performance compared to the other algorithms.”
• A Survey of Physics-Based Attack Detection in Cyber-Physical Systems (2018, background): “Sridhar and Govindarasu [97] were one of the first to consider the case when an ACE signal that cannot be trusted.”