计算机科学
旁道攻击
泄漏(经济)
深度学习
人工智能
信息泄露
生成对抗网络
机器学习
钥匙(锁)
原始数据
生成语法
数据挖掘
模式识别(心理学)
密码学
算法
计算机安全
宏观经济学
经济
程序设计语言
作者
Naila Mukhtar,Lejla Batina,Stjepan Picek,Yinan Kong
标识
DOI:10.1007/978-3-030-95312-6_13
摘要
AbstractDeep learning-based side-channel analysis performance heavily depends on the dataset size and the number of instances in each target class. Both small and imbalanced datasets might lead to unsuccessful side-channel attacks. The attack performance can be improved by generating traces synthetically from the obtained data instances instead of collecting them from the target device, but this is a cumbersome and challenging task.We propose a novel data augmentation approach based on conditional Generative Adversarial Networks (cGAN) and Siamese networks, enhancing the attack capability. We also present a quantitative comparative deep learning-based side-channel analysis between a real raw signal leakage dataset and an artificially augmented leakage dataset. The analysis is performed on the leakage datasets for both symmetric and public-key cryptographic implementations. We investigate non-convergent networks’ effect on the generation of fake leakage signals using two cGAN based deep learning models.The analysis shows that the proposed data augmentation model results in a well-converged network that generates realistic leakage traces, which can be used to mount deep learning-based side-channel analysis successfully even when the dataset available from the device is not optimal. Our results show that the datasets enhanced with “faked” leakage traces are breakable (while not without augmentation), which might change how we perform deep learning-based side-channel analysis. KeywordsDeep learning-based side-channel attacksASCADElliptic curve cryptographyGANsData augmentationSignal processing
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