False Data Injection Attack (FDIA) with Long Sort Term Memory (LSTM) Model using Python

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The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, here we proposed this project a data-driven learning-based Long Short Term Memory (LSTM) algorithm for detecting unobservable FDIAs in systems.

Time Series: Adversarial Attacks Data (Dataset)

API that used in this Project:

• keras
• sklearn
• tensorflow
• matplotlib
• seaborn
• pandas
• numpy

Display Dataset: Adversarial Attacks Data (Time Series)

Plot Dataset: Adversarial Attacks Data (Time Series)

The Accuracy of the LSTM Model (Without FDIA) are Plotted

The loss and Accuracy of the LSTM Model (Without FDIA) are Plotted

Display Dataset With FDIA Attack: Adversarial Attacks Data (Time Series)

The Accuracy of the LSTM Model (Within FDIA) are Plotted

The loss and Accuracy of the LSTM Model (Within FDIA) are Plotted

Comparison of Models: Normal LSTM Model and FDIA LSTM Model