SoftwareApplication
name: AutoEncodersDLSCA
description: Code underlying the publication: Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis
description: 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.
code repository: https://data.4tu.nl/v3/datasets/a286c271-a5eb-41f9-9d3a-357ece7547a1.git
author: Person
  given name: Marina
  family name: Krček
  name: Marina Krček
keywords: Side-channel Analysis
keywords: Portability
keywords: Autoencoders
keywords: Preprocessing
keywords: Hyperparameter Tuning
keywords: transfer learning
programming language: Markdown
programming language: Other
programming language: Python
license:
  creative work: creativeWork
    name: CC-BY-4.0
reference publication: s13389-023-00330-4

s13389-023-00330-4
type: ScholarlyArticle
name: Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis
pagination: 475-497
url: https://doi.org/10.1007%2Fs13389-023-00330-4