Code underlying the publication: Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis

DOI:10.4121/fe14a263-d5f1-4d3e-8d06-b1be95904acf.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/fe14a263-d5f1-4d3e-8d06-b1be95904acf
Datacite citation style:
Krček, Marina (2024): Code underlying the publication: Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/fe14a263-d5f1-4d3e-8d06-b1be95904acf.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

Link to GitHub repository with source code for the publication: Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis.

The source code uses the Python programming language. Scripts used to run the experiments are in the main directory, while the folder 'src' holds the implementations for hyperparameter tuning, loading of side-channel datasets, etc., providing some abstraction. Scripts starting with 'attack' were used to run experiments, while other scripts were helper scripts for analyzing/reading/plotting results.

Sbatch scripts were used to run experiments with TU Delft servers.

More information can be found in the publication.

History

  • 2024-05-14 first online, published, posted

Publisher

4TU.ResearchData

Format

python scripts (.py), slurm sbatch scripts (.sbatch)

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/a286c271-a5eb-41f9-9d3a-357ece7547a1.git "AutoEncodersDLSCA"

Or download the latest commit as a ZIP.