A Comparison of Weight Initializers in Deep Learning-Based Side-Channel Analysis

2020 ◽  
pp. 126-143
Author(s):  
Huimin Li ◽  
Marina Krček ◽  
Guilherme Perin
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

Profiling Attacks against ECC: Side Channel Analysis Based on Deep Learning for Curve-25519

2021 ◽  
Author(s):  
Jiajun Xu ◽  
Meng Li ◽  
Lixin Liang ◽  
Yiwei Zhang ◽  
Shaohua Xiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Recent advances in deep learning‐based side‐channel analysis

ETRI Journal ◽  
2020 ◽  
Vol 42 (2) ◽  
pp. 292-304 ◽  
Author(s):  
Sunghyun Jin ◽  
Suhri Kim ◽  
HeeSeok Kim ◽  
Seokhie Hong
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Recent Advances ◽  
Channel Analysis

scholarly journals Plaintext: A Missing Feature for Enhancing the Power of Deep Learning in Side-Channel Analysis?

2020 ◽  
pp. 49-85
Author(s):  
Anh-Tuan Hoang ◽  
Neil Hanley ◽  
Maire O’Neill
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Body ◽  
Side Channel ◽  
Secret Key ◽  
Side Channel Analysis ◽  
Filter Kernel ◽  
Secret Keys ◽  
Channel Analysis ◽  
Template Attacks

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various model architectures for object classification. Straight-forward application of DL to side-channel analysis (SCA) has already shown promising success, with experimentation on open-source variable key datasets showing that secret keys can be revealed with 100s traces even in the presence of countermeasures. This paper aims to further improve the application of DL for SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a new model, CNN-based model with Plaintext feature extension (CNNP) together with multiple convolutional filter kernel sizes and structures with deeper and narrower neural networks, which has empirically proven its effectiveness by outperforming reference profiling attack methods such as template attacks (TAs), convolutional neural networks (CNNs) and multilayer perceptron (MLP) models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 40 attack traces in comparison with order of 100s traces required by straightforward machine learning (ML) application. During the profiling stage an attacker needs no additional knowledge on the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key. Additionally, no heuristic pre-processing is required in order to break the high-order masking countermeasures of the target implementation.

Deep Learning Techniques for Side-Channel Analysis

2021 ◽  
pp. 255-269
Author(s):  
Varsha Satheesh Kumar ◽  
S. Dillibabu Shanmugam ◽  
N. Sarat Chandra Babu
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Learning Techniques ◽  
Channel Analysis

Deep learning for side-channel analysis and introduction to ASCAD database

2019 ◽  
Vol 10 (2) ◽  
pp. 163-188 ◽  
Author(s):  
Ryad Benadjila ◽  
Emmanuel Prouff ◽  
Rémi Strullu ◽  
Eleonora Cagli ◽  
Cécile Dumas
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Deep Learning Side-Channel Analysis on Large-Scale Traces

2020 ◽  
pp. 440-460
Author(s):  
Loïc Masure ◽  
Nicolas Belleville ◽  
Eleonora Cagli ◽  
Marie-Angela Cornélie ◽  
Damien Couroussé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders

2020 ◽  
pp. 389-415
Author(s):  
Lichao Wu ◽  
Stjepan Picek
Keyword(s):  
Deep Learning ◽  
Detailed Analysis ◽  
Side Channel ◽  
Channel Measurements ◽  
Denoising Autoencoder ◽  
Side Channel Analysis ◽  
Learning Technique ◽  
Different Types ◽  
Channel Analysis

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further combined to make the attacks even more challenging. An intuitive solution to improve the performance of attacks would be to reduce the effect of countermeasures.This paper investigates whether we can consider certain types of hiding countermeasures as noise and then use a deep learning technique called the denoising autoencoder to remove that noise. We conduct a detailed analysis of six different types of noise and countermeasures separately or combined and show that denoising autoencoder improves the attack performance significantly.

scholarly journals Electromagnetic and Power Side-Channel Analysis: Advanced Attacks and Low-Overhead Generic Countermeasures through White-Box Approach

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 30
Author(s):  
Debayan Das ◽  
Shreyas Sen
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Side Channel ◽  
Secret Key ◽  
Embedded Devices ◽  
Side Channel Analysis ◽  
Root Cause ◽  
Channel Analysis ◽  
Metal Layers ◽  
A Current

Electromagnetic and power side-channel analysis (SCA) provides attackers a prominent tool to extract the secret key from the cryptographic engine. In this article, we present our cross-device deep learning (DL)-based side-channel attack (X-DeepSCA) which reduces the time to attack on embedded devices, thereby increasing the threat surface significantly. Consequently, with the knowledge of such advanced attacks, we performed a ground-up white-box analysis of the crypto IC to root-cause the source of the electromagnetic (EM) side-channel leakage. Equipped with the understanding that the higher-level metals significantly contribute to the EM leakage, we present STELLAR, which proposes to route the crypto core within the lower metals and then embed it within a current-domain signature attenuation (CDSA) hardware to ensure that the critical correlated signature gets suppressed before it reaches the top-level metal layers. CDSA-AES256 with local lower metal routing was fabricated in a TSMC 65 nm process and evaluated against different profiled and non-profiled attacks, showing protection beyond 1B encryptions, compared to ∼10K for the unprotected AES. Overall, the presented countermeasure achieved a 100× improvement over the state-of-the-art countermeasures available, with comparable power/area overheads and without any performance degradation. Moreover, it is a generic countermeasure and can be used to protect any crypto cores while preserving the legacy of the existing implementations.

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