Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach

Naila Mukhtar; Yinan Kong

doi:10.1007/978-3-319-94421-0_6

Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach

Naila Mukhtar^*, Yinan Kong

^*Corresponding author for this work

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

2 Citations (Scopus)

Abstract

Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.

Original language	English
Title of host publication	Future network systems and security
Subtitle of host publication	4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings
Editors	Robin Doss, Selwyn Piramuthu, Wei Zhou
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	80-92
Number of pages	13
ISBN (Electronic)	9783319944210
ISBN (Print)	9783319944203
DOIs	https://doi.org/10.1007/978-3-319-94421-0_6
Publication status	Published - 2018
Event	4th International Conference on Future Network Systems and Security, FNSS 2018 - Paris, France Duration: 9 Jul 2018 → 11 Jul 2018

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer Nature
Volume	878
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	4th International Conference on Future Network Systems and Security, FNSS 2018
Country	France
City	Paris
Period	9/07/18 → 11/07/18

Keywords

Side-Channel analysis
Embedded system security
Signal-processing
Machine-learning classification
Neural-network classification

Access to Document

10.1007/978-3-319-94421-0_6

Link to publication in Scopus

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Mukhtar, N., & Kong, Y. (2018). Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. In R. Doss, S. Piramuthu, & W. Zhou (Eds.), Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings (pp. 80-92). (Communications in Computer and Information Science; Vol. 878). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-94421-0_6

Mukhtar, Naila ; Kong, Yinan. / Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings. editor / Robin Doss ; Selwyn Piramuthu ; Wei Zhou. Cham : Springer, Springer Nature, 2018. pp. 80-92 (Communications in Computer and Information Science).

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title = "Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach",

abstract = "Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.",

keywords = "Side-Channel analysis, Embedded system security, Signal-processing, Machine-learning classification, Neural-network classification",

author = "Naila Mukhtar and Yinan Kong",

year = "2018",

doi = "10.1007/978-3-319-94421-0_6",

language = "English",

isbn = "9783319944203",

series = "Communications in Computer and Information Science",

publisher = "Springer, Springer Nature",

pages = "80--92",

editor = "Robin Doss and Selwyn Piramuthu and Wei Zhou",

booktitle = "Future network systems and security",

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note = "4th International Conference on Future Network Systems and Security, FNSS 2018 ; Conference date: 09-07-2018 Through 11-07-2018",

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Mukhtar, N & Kong, Y 2018, Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. in R Doss, S Piramuthu & W Zhou (eds), Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings. Communications in Computer and Information Science, vol. 878, Springer, Springer Nature, Cham, pp. 80-92, 4th International Conference on Future Network Systems and Security, FNSS 2018, Paris, France, 9/07/18. https://doi.org/10.1007/978-3-319-94421-0_6

Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. / Mukhtar, Naila; Kong, Yinan.

Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings. ed. / Robin Doss; Selwyn Piramuthu; Wei Zhou. Cham : Springer, Springer Nature, 2018. p. 80-92 (Communications in Computer and Information Science; Vol. 878).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Kong, Yinan

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N2 - Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.

AB - Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.

KW - Side-Channel analysis

KW - Embedded system security

KW - Signal-processing

KW - Machine-learning classification

KW - Neural-network classification

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Mukhtar N, Kong Y. Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. In Doss R, Piramuthu S, Zhou W, editors, Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings. Cham: Springer, Springer Nature. 2018. p. 80-92. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-94421-0_6