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# SecureGrid Deep Learning based Attack Detection System for Smart Grids
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This is the software of Deep Learning based Attack Detection System for Smart Grids
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It is composed of three modules, which should be executed in order:
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* `simulator/`  - Data generation of the power of residential houses ander attacks
* `data-preprocessing/` - Data preprocessing of the results of the simulator
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* `attack-detector/` - deep learning module for detecting attacks 
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Please, refer to every module for its installation, configuration and execution.
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The directory `data` contains the result of the execution of the three modules when the simulation has been set to an `attackPercentageValue` of 0, 10, 20 and 30.
It is organized as follows:
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- normal2/ (0 attacks)
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    + demo_0.hdf5 (generated by simulator)
    + house_N.pkl (one file for each house generated by data-preprocessing, with N in 0..37)
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- anomaly_20_2, anomaly_30_2, files with attacks.