A Python library to perform Automated Side-Channel Attacks using Black-Box Neural Architecture Search

Package

The deepscapy is a python package for automatically finding optimal architectures when performing a side channel attack. Its Ranking Loss (RKL) implementation is based on https://github.com/gabzai/Ranking-Loss-SCA.

Installation

You can install deepsca using::

python setup.py install

Dependencies

deepscapy depends on NumPy, SciPy, matplotlib, scikit-learn, joblib and tqdm, tensorflow, tensorflow_addons, keras_tuner, keras, autokeras. For data processing and generation you will also need and pandas.

License

Apache License, Version 2.0

Datasets

The datasets used in the paper "Automated Side-Channel Attacks using Black-Box Neural Architecture Search" can be found at https://drive.google.com/drive/folders/1GcWQvwwEdbj2L0c1hd2YpLpbS-gIFJJ5. The following table shows their properties:

Dataset name	# Features	# Profiling traces	# Attack traces	Attack byte	URL
ASCAD_f	700	50000	10000	2	ASCAD.h5 from https://github.com/ANSSI-FR/ASCAD/tree/master/ATMEGA_AES_v1/ATM_AES_v1_fixed_key
ASCAD_f desync50	700	50000	10000	2	ASCAD_desync50.h5 from "
ASCAD_f desync100	700	50000	10000	2	ASCAD_desync100.h5 from "
ASCAD_r	1400	200000	100000	2	ASCAD.h5 from https://github.com/ANSSI-FR/ASCAD/tree/master/ATMEGA_AES_v1/ATM_AES_v1_variable_key/
ASCAD_r desync50	1400	200000	100000	2	ASCAD_desync50.h5 from "
ASCAD_r desync100	1400	200000	100000	2	ASCAD_desync100.h5 from "
CHES CTF	2200	45000	5000	2	http://aisylabdatasets.ewi.tudelft.nl/ches_ctf.h5
AES_HD	1250	50000	25000	0	https://github.com/gabzai/Methodology-for-efficient-CNN-architectures-in-SCA/blob/master/AES_HD/AES_HD_dataset.zip
AES_RD	3500	25000	25000	0	https://github.com/gabzai/Methodology-for-efficient-CNN-architectures-in-SCA/tree/master/AES_RD/AES_RD_dataset
DPAv4	4000	4500	500	0	https://github.com/gabzai/Methodology-for-efficient-CNN-architectures-in-SCA/blob/master/DPA-contest%20v4/DPAv4_dataset.zip

More datasets of hardware side-channel attacks can be found at https://github.com/ITSC-Group/sca-datasets.

Search Space

The search space used in the paper "Automated Side-Channel Attacks using Black-Box Neural Architecture Search" is already defined in this project. These are the relevant parameter ranges:

Hyperparameter Type	Hyperparameter	Possible Options
Whole Network	Optimizer	{'adam' , 'adam_with_weight_decay'}
	Learning rate	{1e-1, 5e-2, 1e-2, 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, 1e-5}
Every Layer	Dropout	{0.0, 0.1, 0.2, 0.3, 0.4, 0.5}
	Use Batch Normalization	{True, False}
	Activation Function	{'relu', 'selu', 'elu', 'tanh'}
Convolutional Block	# of Blocks	{1, 2, 3, 4, 5}
	Convolutional Kernel Size	{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}
	Convolutional Filters	{2, 8, 16, 32, 64, 128, 256}
	Pooling Type	{'max' , 'average'}
	Pooling Strides 1D CNN	{2, 3, 4, 5, 6, 7, 8, 9, 10}
	Pooling Poolsize 1D CNN	{2, 3, 4, 5}
	Pooling Strides 2D CNN	{2, 4}
	Pooling Poolsize 2D CNN	Convolutional Kernel Size-1
Dense Block	# of Blocks	{1, 2, 3}
	Hidden Units	{2, 4, 8, 16, 32, 64, 128, 256, 512, 1024}