http://iet.metastore.ingenta.com
1887

Convolutional neural network attack on cryptographic circuits

Convolutional neural network attack on cryptographic circuits

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this Letter, a novel convolutional neural network (CNN) attack is explored as a new kind of malicious attack to disclose the secret key of cryptographic circuits. A cryptographic circuit with the known secret key is set as the reference model for training the CNNs. Moreover, a matrix that includes the information of an input plaintext and the known secret key of the cryptographic circuit is built as the input training data of the CNNs. A Sigmoid function and a step function are used to normalise and classify the power dissipation of the cryptographic circuit to generate the output training data of the CNNs, respectively. After training the CNNs, a cryptographic circuit with the unknown secret key can be cracked by hypothesising all the possible keys to test the well-trained CNNs, because the correct secret key enables the CNNs to achieve the highest testing accuracy among all the hypothesised keys. As demonstrated in the results, the proposed CNN attack successfully reveals the secret key of a unprotected (protected) cryptographic circuit after analysing about 500 (100,000) data.

http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.8024
Loading

Related content

content/journals/10.1049/el.2018.8024
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address