TripletPower: Deep-Learning Side-Channel Attacks over Few Traces
TripletPower: Deep-Learning Side-Channel Attacks over Few Traces
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DOI：
10.1109/host55118.2023.10133495
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Chenggang Wang;Jimmy Dani;S. Reilly;Austen Brownfield;Boyang Wang;J. Emmert
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文献类型：
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作者：
Chenggang Wang;Jimmy Dani;S. Reilly;Austen Brownfield;Boyang Wang;J. Emmert
深度学习已被用作侧信道攻击中的一种有前途的技术。然而，为了成功恢复密钥，深度学习侧信道攻击通常需要数千个训练轨迹，这对攻击者来说在真实的世界中获得可能是一个挑战。本文提出了一种新的深度学习侧信道攻击，它只需要数百个训练轨迹。我们提出的方法被称为TripletPower，它训练了一个三元组网络，该网络学习了一个鲁棒的嵌入，用于几乎没有痕迹的侧信道攻击。我们展示了我们的方法在分析攻击的优势，从AVR XMEGA和ARM STM32微控制器使用ChipWhisperer收集的电源跟踪。具体来说，实验结果表明，我们的方法只需要低至250个训练痕迹，以训练一个分类器成功地恢复密钥的未掩蔽AES上的XMEGA（或STM 32），而卷积神经网络需要至少4,000训练痕迹的剖析攻击。此外，我们将我们的方法扩展到非分析攻击与飞行标签。实验结果表明，该方法可以有效地恢复密钥的未掩蔽AES在XMEGA上只有525个未标记的功率迹线在非仿形攻击。我们的方法也是有效的电源跟踪从屏蔽AES和随机延迟产生的痕迹。
Deep learning has been utilized as a promising technique in side-channel attacks. However, to recover keys successfully, deep-learning side-channel attacks often require thousands of training traces, which could be challenging for an attacker to obtain in the real world. This paper proposes a new deep-learning side-channel attack which only requires hundreds of training traces. Our proposed method, referred to as TripletPower, trains a triplet network, which learns a robust embedding for side-channel attacks with few traces. We demonstrate the advantage of our method in profiling attacks over power traces collected from AVR XMEGA and ARM STM32 microcontrollers using ChipWhisperer. Specffically, experimental results show that our method only needs as low as 250 training traces to train a classffier successfully recovering keys of unmasked AES on XMEGA (or STM32) while a Convolutional Neural Network needs at least 4,000 training traces in profiling attacks. In addition, we extend our method to non-profiling attacks with on-the-fly labeling. Experimental results suggest that our method can effectively recover keys of unmasked AES on XMEGA with only 525 unlabeled power traces in non-profiling attacks. Our method is also effective over power traces collected from masked AES and traces generated with random delay.