| Title | A Stealthier False Data Injection Attack against the Power Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yan, Weili, Lou, Xin, Yau, David K.Y., Yang, Ying, Saifuddin, Muhammad Ramadan, Wu, Jiyan, Winslett, Marianne |
| Conference Name | 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
| Date Published | oct |
| Keywords | adaptive control, Automatic frequency control, automatic generation control, Computers, Conferences, false trust, Forecasting, policy-based governance, pubcrawl, resilience, Resiliency, Scalability, Smart grids |
| Abstract | We use discrete-time adaptive control theory to design a novel false data injection (FDI) attack against automatic generation control (AGC), a critical system that maintains a power grid at its requisite frequency. FDI attacks can cause equipment damage or blackouts by falsifying measurements in the streaming sensor data used to monitor the grid's operation. Compared to prior work, the proposed attack (i) requires less knowledge on the part of the attacker, such as correctly forecasting the future demand for power; (ii) is stealthier in its ability to bypass standard methods for detecting bad sensor data and to keep the false sensor readings near historical norms until the attack is well underway; and (iii) can sustain the frequency excursion as long as needed to cause real-world damage, in spite of AGC countermeasures. We validate the performance of the proposed attack on realistic 37-bus and 118-bus setups in PowerWorld, an industry-strength power system simulator trusted by real-world operators. The results demonstrate the attack's improved stealthiness and effectiveness compared to prior work. |
| DOI | 10.1109/SmartGridComm51999.2021.9632337 |
| Citation Key | yan_stealthier_2021 |
A Stealthier False Data Injection Attack against the Power Grid