NDLI: Boosting the efficacy of power attacks on cryptographic circuits with autoencoder

Content Provider	IET Digital Library
Author	Wen, Yiming Yu, Weize
Abstract	Power attacks are able to leak the secret key of protected cryptographic circuits as long as the number of sampled power data is sufficient to filter the injected noise. However, in this Letter, a novel power attack is proposed to break protected cryptographic circuits when the number of available power data is inadequate for filtering the injected noise. To achieve this goal, a state-of-the-art machine learning technique: autoencoder is utilised for generating a certain number of new power data that are similar to the original power data to assist the noise filtering. Within the proposed autoencoder neural networks, the input and output layers are set with 1024 × 1 arrays. Furthermore, two convolution layers are used in the encoder block and decoder block, respectively, to achieve similar features between the input layer and the output layer. Under the assistance of autoencoder, the novel power attack reveals the secret key of a protected cryptographic circuit with 300,000 power data. In contrast, the regular power attacks fail to break the cryptographic circuit under the same number of power data.
Starting Page	1221
Ending Page	1224
Page Count	4
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 23, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.2463
Journal	Electronics Letters
Publisher Date	2019-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	000 Power Data 300 Power Data Autoencoder Available Power Data Cryptography Data Security Encoding Injected Noise Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Noise Filtering Original Power Data Power Attack Protected Cryptographic Circuit Regular Power Attack Sampled Power Data Secret Key Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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