| Title | Autoencoder Based FDI Attack Detection Scheme For Smart Grid Stability |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | G, Amritha, Kh, Vishakh, C, Jishnu Shankar V, Nair, Manjula G |
| Conference Name | 2022 IEEE 19th India Council International Conference (INDICON) |
| Keywords | and Cyber-Physical Systems (CPS), Autoencoders (AE’s), composability, CPS modeling, Cyber-physical systems, Data models, False Data Injection (FDI), false data injection attack (FDIA), machine learning, machine learning algorithms, Metrics, power system stability, pubcrawl, resilience, Resiliency, simulation, Stability analysis |
| Abstract | One of the major concerns in the real-time monitoring systems in a smart grid is the Cyber security threat. The false data injection attack is emerging as a major form of attack in Cyber-Physical Systems (CPS). A False data Injection Attack (FDIA) can lead to severe issues like insufficient generation, physical damage to the grid, power flow imbalance as well as economical loss. The recent advancements in machine learning algorithms have helped solve the drawbacks of using classical detection techniques for such attacks. In this article, we propose to use Autoencoders (AE's) as a novel Machine Learning approach to detect FDI attacks without any major modifications. The performance of the method is validated through the analysis of the simulation results. The algorithm achieves optimal accuracy owing to the unsupervised nature of the algorithm. |
| DOI | 10.1109/INDICON56171.2022.10040183 |
| Citation Key | g_autoencoder_2022 |
Autoencoder Based FDI Attack Detection Scheme For Smart Grid Stability