Learning-Based Defense of False Data Injection Attacks in Power System State Estimation
| Autor: | Abhijeet Sahu, Erchin Serpedin, Katherine Davis, Arnav Kundu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
020203 distributed computing
Artificial neural network Computer science business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Grid Computer security computer.software_genre Telecommunications network Electric power system Transmission (telecommunications) Control system 0202 electrical engineering electronic engineering information engineering State (computer science) Artificial intelligence business computer |
| Zdroj: | 2019 North American Power Symposium (NAPS). |
| DOI: | 10.1109/naps46351.2019.9000216 |
| Popis: | The electric power grid has evolved immensely with time and the modern power grid is dependent on communication networks for efficient transmission and distribution. Since communication networks are vulnerable to various kinds of cyber attacks it is important to detect them and prevent an important machinery like the power grid to get affected from cyber attacks. False data injection attacks (FDIA) are one of the most common attack strategies where an attacker tries to trick the underlying control system of the grid, by injecting false data in sensor measurements to cause disruptions. Our work has focused towards Least Effort attacks of two types i.e., Random and Target Attacks. Further, we propose a data augmented deep learning based solution to detect such attacks in real time. We aim at generating realistic attack simulations on standard IEEE 14 architectures and train neural networks to detect such attacks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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