AISyLab @ TU Delft

side-channel-analysis-toolbox. This is a project in which side-channel attacks are researched and developed.

AISY_Framework. Deep Learning-based Framework for Side-Channel Analysis

EnsembleSCA. Repository code to support paper TCHES2020 paper "Strength in Numbers: Improving Generalization with Ensembles in Machine Learning-based Profiled Side-channel Analysis"

Triplet-attack. Python

Denoising-autoencoder. Repository code to support paper TCHES2020 paper "Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders"

feature_selection_dlsca. Exploring Feature Selection1 Scenarios for Deep Learning-based Side-Channel Analysis

Reinforcement-Learning-for-SCA. Python

AutoSCA. Python

AES_HD. AES HD dataset

IterativeDLFramework. Keep it unsupervised: Horizontal Attacks meet Deep Learning

MORE-for-Unsupervised-SCA. Source code for: "The Need for MORE: Unsupervised Side-channel Analysis with Single Network Training and Multi-output Regression". COSADE 2024

Label-distribution-and-correlation. Python

fge_sca. A Fast Guessing Entropy Calculation for Deep Learning-based SCA

focal_loss. Focal Loss Function for Deep Learning-based Side-channel Analysis

AES_HD_Ext. AES HD Dataset 2

DataAugmentationDLSCA. Deep Learning-Based Profiling SCA with Data Augmentation Techniques

lth_sca. Lottery Ticket Hypothesis for Profiled Side-Channel Analysis