| Title | LSTM-Based False Data Injection Attack Detection in Smart Grids |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhao, Yi, Jia, Xian, An, Dou, Yang, Qingyu |
| Conference Name | 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-7684-0 |
| Keywords | Classification algorithms, composability, cyber physical systems, Cyber-physical systems, Data models, energy management systems, False Data Detection, false data injection attack, feature extraction, Human Behavior, human factors, LSTM, Neural networks, pubcrawl, resilience, Resiliency, Smart grid, Smart grids, Transforms |
| Abstract | As a typical cyber-physical system, smart grid has attracted growing attention due to the safe and efficient operation. The false data injection attack against energy management system is a new type of cyber-physical attack, which can bypass the bad data detector of the smart grid to influence the results of state estimation directly, causing the energy management system making wrong estimation and thus affects the stable operation of power grid. We transform the false data injection attack detection problem into binary classification problem in this paper, which use the long-term and short-term memory network (LSTM) to construct the detection model. After that, we use the BP algorithm to update neural network parameters and utilize the dropout method to alleviate the overfitting problem and to improve the detection accuracy. Simulation results prove that the LSTM-based detection method can achieve higher detection accuracy comparing with the BPNN-based approach. |
| URL | https://ieeexplore.ieee.org/document/9337674 |
| DOI | 10.1109/YAC51587.2020.9337674 |
| Citation Key | zhao_lstm-based_2020 |
LSTM-Based False Data Injection Attack Detection in Smart Grids