tanvirarafin/ascad-5minutes

An attack of the ASCAD dataset in 5 minutes

Attacking ASCAD with a Single Trace

This repository contains the scripts associated to the paper Give Me 5 Minutes: Attacking ASCAD with a Single Side-Channel Trace.

Installation

This repository contains simple python (>=3.7) scripts, with known-good dependencies specified in requirements.txt. A recent version of pip is needed if you want to install the pre-build version of SCALib, otherwise pip will try to recompile SCALib from scratch (which will fail if you don't have the build dependencies installed).

We suggest using a virtual environment:

git clone https://github.com/cassiersg/ASCAD-5minutes.git
cd ASCAD-5minutes
python3 -m venv ve

# On linux
source ve/bin/activate
# On windows
RUN ve/Scripts/activate

pip install -U pip # to get recent pip
pip install -r requirements.txt

Usage

attack.py

This is a simple attack script which minimally reproduces our attack and should be fairly esay to read or modify. To run with the default settings:

python3 attack.py --database /path/to/ASCAD/raw/traces/atmega8515-raw-traces.h5

For other attack settings:

python3 attack.py --help

attack_multi.py

This script performs the same attacks as <attack.py>, but stores intermediate results, run multiple attacks in parallel and produces success rate plots.

Commands used to generate the success rate figures in the paper:

Full trace and full key:

python3 attack_multi.py --database ./atmega8515-raw-traces.h5 --fast-snr --ntracesattack 1 --averageattack 1000 --poi 32,64,128,256,512,1024,2048 --dim 1,2,3,4,5,6,7,8,9,10,15,20  --show snr,model,attack,sr-map

Partial trace and one byte of key:

python3 attack_multi.py --database ./atmega8515-raw-traces.h5 --fast-snr --ntracesattack 1,2,4,8,16,32 --nbytes 1 --averageattack 1000  --ntracesprofile 20000 --window 80945,82345 --show snr,model,attack,sr-boxplot

For all attack settings:

python3 attack_multi.py --help

subset_traces.py

A utility script to make subsets of the ASCAD database that are smaller, thus faster to load, for faster experimentation.

python3 subset_traces.py --help

License

MIT