| Title | A Bagging MLP-based Autoencoder for Detection of False Data Injection Attack in Smart Grid |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Paul, Shuva, Kundu, Ripan Kumar |
| Conference Name | 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
| Keywords | Complexity theory, composability, Data models, decision making, etc, False Data Detection, false data injection attacks, Forestry, Human Behavior, Labeling, machine learning, pubcrawl, resilience, Resiliency, security, Smart grid, Smart grids |
| Abstract | The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control decisions are generated based on the data the Smart Grid generates during its operation. This data can be tampered with or attacked in communication lines to mislead the control room in decision-making. The false data injection attack (FDIA) is one of the most severe cyberattacks on today's cyber-physical power system, as it has the potential to cause significant physical and financial damage. However, detecting cyberattacks are incredibly challenging since they have no known patterns. In this paper, we launch a random FDIA on IEEE-39 bus system. Later, we propose a Bagging MLP-based autoencoder to detect the FDIAs in the power system and compare the result with a single ML model. The Bagging MLP-based autoencoder outperforms the Isolation forest while detecting FDIAs. |
| DOI | 10.1109/ISGT50606.2022.9817480 |
| Citation Key | paul_bagging_2022 |
A Bagging MLP-based Autoencoder for Detection of False Data Injection Attack in Smart Grid