Convolutional neural network based sidechannel attacks

Authors

  • Tran Ngoc Quy
  • Nguyen Thanh Tung
  • Do Quang Trung
  • Dang Hung Viet

DOI:

https://doi.org/10.54654/isj.v1i15.834

Keywords:

Side-channel attack, Profiled attack, machine learning

Tóm tắt

Abstract—The profiled attack is considered one of
the most effective side-channel attacks (SCA)
methods used to reveal the secret key and evaluate
the security of the cryptographic devices. By
considering a classification problem, profiled SCA
can be successfully conducted by machine learning
techniques, as shown by recent works. However,
these studies only provide general principles of the
attack. Therefore, this paper presents technical
aspects and specific instructions for an attacker
when performing a profiled attack on a specific
cryptographic device using a popular deep
learning technique called convolution neural
network. The experimental process and the results
of the attack on AES-128 are presented to prove
the effectiveness of the attack procedure.
Tóm tắt—Trong các phương pháp tấn công
kênh kề, tấn công mẫu được xem là một trong các
phương pháp hiệu quả được sử dụng để tìm khóa
bí mật và đánh giá độ an toàn của thiết bị mật mã.
Bài toán tấn công mẫu có điểm tương đồng với bài
toán phân lớp sử dụng các kỹ thuật học máy, học
sâu. Các nghiên cứu về tấn công mẫu gần đây chỉ
ra rằng có thể áp dụng thành công kỹ thuật học
sâu khác nhau vào quy trình của cuộc tấn công
mẫu. Tuy nhiên các nghiên cứu này chỉ đưa ra
nguyên lý chung của tấn công. Do đó, bài báo này
đề xuất một quy trình tấn công cụ thể bao gồm các
khía cạnh kỹ thuật, các chỉ dẫn cụ thể cho người
tấn công khi thực hiện cuộc tấn công mẫu trên
thiết bị mật mã cụ thể sử dụng một kỹ thuật học
sâu phổ biến là mạng nơ-ron tích chập. Quá trình
thực nghiệm và kết quả tấn công trên AES-128
cũng được trình bày để chứng minh tính hiệu dụng
của quy trình tấn công đề xuất.

Downloads

Download data is not yet available.

References

Kocher P, Jaffe J, Jun B, "Differential Power

Analysis," CRYPTO 1999, LNCS 1666.

Springer: Heidelberg, p. 388–397, 1999.

BRIER, E., CLAVIER, C., OLIVIER, F,

"Correlation power analysis with a leakage

model," in Proceedings of the International

Workshop on Cryptographic Hardware and

Embedded Systems, Cambridge (USA), 2004.

Chari S, Rao JR, Rohatgi P, "Template Attacks,"

CHES 2002, LNCS 2523. Springer: Heidelberg,

pp. 13-28, 2002.

A. Heuser and M. Zohner, "Intelligent Machine

Homicide Breaking Cryptographic Devices

Using Support Vector," in COSADE 2012,

Heidelberg, 2012.

Bartkewitz, T., Lemke-Rust, K, "Efficient

template attacks based on probabilistic multiclass support vector machines," in Mangard, S.

(ed.) Smart Card Research and Advanced

Applications:11th International Conference,

CARDIS 2012, Graz, Austria, 2012.

Emmanuel Prouff, Remi Strullu, Ryad

Benadjila, Eleonora Cagli, and Cecile Dumas,

"Study of deep learning techniques for sidechannel analysis and introduction to ascad

database," Cryptology ePrint Archive, Report

/053, 2018.

https://eprint.iacr.org/2018/053, 2018.

Gabriel Zaid, Lilian Bossuet, Amaury Habrard,

and Alexandre Venelli, "Methodology for

efficient cnn architectures in profiling attacks,"

Cryptology ePrint Archive, 2019.

Houssem Maghrebi, Thibault Portigliatti, and

Emmanuel Prouff, "Breaking cryptographic

implementations using deep learning

techniques," in In Claude Carlet, M. Anwar

Hasan, and Vishal Saraswat, editors, Security,

Privacy, and Applied Cryptography

Engineering, Springer International Publishing.

ISBN 978-3-319-49445-6, 2016, pp. 3-26.

Cagli E., Dumas C., Prouff E, "onvolutional

Neural Networks with Data Augmentation

Against Jitter-Based Countermeasures," in

Fischer W., Homma N. (eds) Cryptographic

Hardware and Embedded Systems – CHES

, Lecture Notes in Computer Science, vol

Springer, Cham, 2017.

Coron, J. ,Kizhvatov, I., "An Efficient Method

for Random Delay Generation in Embedded

Software," in CHES 2009, 2009.

Tran, N.Q., Nguyen, H.Q., "Efficient cnn-based

profiled side-channel attacks," Journal of

Computer Science and Cybernetics, vol. 37, no.

, pp. 1-22, 2021.

Standaert FX., Malkin T.G., Yung M., "A

Unified Framework for the Analysis of SideChannel Key Recovery Attacks," in In: Joux A.

(eds) Advances in Cryptology - EUROCRYPT

EUROCRYPT 2009. Lecture Notes in

Computer Science, vol 5479, Springer, Berlin,

Heidelberg, 2009.

Benadjila, R., Prouff, E., Strullu, R., Cagli, E.,

Dumas, C, "Deep learning for side-channel

analysis and introduction to ASCAD database,"

J. Cryptographic Engineering, vol. 10, no. 2, pp.

-188, 2020.

Downloads

Abstract views: 0 / PDF downloads: 0

Published

2022-06-08

How to Cite

Quy, T. N., Tung, N. T., Trung, D. Q., & Viet, D. H. (2022). Convolutional neural network based sidechannel attacks. Journal of Science and Technology on Information Security, 1(15), 26-37. https://doi.org/10.54654/isj.v1i15.834

Issue

Vol. 1. No. (15) 2022

Section

Papers

License

Proposed Policy for Journals That Offer Open Access

Authors who publish with this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Proposed Policy for Journals That Offer Delayed Open Access

Authors who publish with this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication, with the work [SPECIFY PERIOD OF TIME] after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).