XTM
False Data Injection Attack Detection in Smart Grid
Install / Use
/learn @gcsarker/XTMREADME
XTM
<p> This Repository is accompanied by the paper titled, </p><p> False data injection (FDI) attack in smart grid can cause catastrophic impact in energy management and distribution. Here, a novel hybrid model combining the state of the art transformer and LSTM is developed to detect the presence of FDI as well as the location of attack in smart grid. </p><a href = "https://www.mdpi.com/2079-9292/12/4/797">XTM: A Novel Transformer and LSTM-Based Model for Detection and Localization of Formally Verified FDI Attack in Smart Grid</a>.
Dataset:
<p> The initial dataset consists of hourly historical sensor measurements of one year. we have tested our model on <b>IEEE-14 bus system </b>. So, So, we have taken 54 measurements. We have generated the attack vector in accordance with <a href = "https://ieeexplore.ieee.org/abstract/document/9705034/">this article </a>. Further we have extended the hourly dataset to minutely dataset. Both hourly, minutely data and the generated attack vector can be accessed from <a href = "https://drive.google.com/drive/folders/1Z5m7lIJZFJuL_2wvzQ7hL_pYDETxy9uN?usp=sharing"> google drive </a>. </p>
Setting Up
The model is developed on the following system environments,
- python 3.8
- tensorflow 2.7.
Use the following steps to run the model. In the future update the readme file will be modified accordingly.
- clone the repository into the local machine.
- download the data files from drive link above and into the dataset directory.
- run main.py
