Make your first side-channel attack on public datasets with eShard. This is a mirror of scared Gitlab repository. All contributions and merge request must be done through Gitlab project.

A public repo for the datasets recorded as part of "A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards".

Collection of metadata of different Side Channel Attack datasets

A Dataset for Segmenting and Attacking Obfuscated Power Traces in Side-Channel Analysis

A cache side-channel attack dataset based on stress-ng.

Fixed-Key AESv1 byte-2 AI SCA trained and tested using the ASCAD dataset

Dataset of NVLink latency traces used to evaluate side-channel attack in "SideLink: Exposing NVLink to Covert and Side-Channel Attacks."

This is a dataset for side-channel attacks against the Dilithium algorithm.

An Simple implementation of Side Channel attacks based on the AES2 datasets

Dataset and machine learning models from the paper "Oriole" to detect cache side-channel attacks.

This project implements differential privacy at student disciplinary dataset from New Mexico school districts (2010-11 to 2021-22). This repository contains differential privacy implementation code, side channel attack, and methods to patch side channel attack.

Python toolkit implementing 11 side-channel attacks (DPA, CPA, MIA, LRA, polynomial, unsupervised, and second-order) against the ANSSI ASCAD benchmark dataset.

Example code and datasets for "Side-Channel Attacks Revisited — an Optimization Problem Perspective: Bootstrapping and Space Reduction". Submitted under a throwaway GitHub account/ email.

PyTorch framework for profiling side-channel attacks on AES, featuring Quadrant Scan–based preprocessing and convolutional-Transformer models, evaluated using Guessing Entropy (GE) and NTGE on CHES-style datasets.

Implementation and comparative analysis of 10 different Deep Learning architectures (MLP, CNN, ResNet) for Side-Channel Attacks on the ASCAD dataset. Focuses on Key Rank and Guessing Entropy metrics.

Study & toolkit for analyzing WPA3/SAE security. Contains Python notebooks, attack-simulation code, datasets, and two papers (thesis + dissertation) that reproduce and extend “Dragonblood” timing/cache side-channel research. Use to explore handshake internals, plot collected traces, and test counter-measures.

In the paper titled "Uncover Secrets Through the Cover: A Deep Learning-based Side-channel Attack against Kyber Implementations with Anti-Tampering Covers," the authors have established this GitHub repository to store the code, datasets, and experimental results used in their research.