| Title | False Data Detection Based On LSTM Network In Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Xu, Ben, Liu, Jun |
| Conference Name | 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) |
| Date Published | mar |
| Keywords | Computational modeling, Computers, detection, FDI attacks, LSTM, Metrics, Monitoring, Neural networks, pubcrawl, resilience, Resiliency, Scalability, Smart grid, smart grid security, Smart grids, software engineering, Standards |
| Abstract | In contrast to traditional grids, smart grids can help utilities save energy, thereby reducing operating costs. In the smart grid, the quality of monitoring and control can be fully improved by combining computing and intelligent communication knowledge. However, this will expose the system to FDI attacks, and the system is vulnerable to intrusion. Therefore, it is very important to detect such erroneous data injection attacks and provide an algorithm to protect the system from such attacks. In this paper, a FDI detection method based on LSTM has been proposed, which is validated by the simulation on the ieee-14 bus platform. |
| DOI | 10.1109/AEMCSE51986.2021.00073 |
| Citation Key | xu_false_2021 |
False Data Detection Based On LSTM Network In Smart Grid