Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems

Yaoyuan Hu; An Wang; Weiping Gong; Jingjie Wu; Ziyu Wang; Shiming Zhang; Shufan Ma

doi:10.1007/978-3-031-61486-6_21

Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems

Yaoyuan Hu, An Wang^*, Weiping Gong^*, Jingjie Wu^*, Ziyu Wang, Shiming Zhang, Shufan Ma

^*Corresponding author for this work

School of Cyberspace Science and Technology

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

Abstract

Simple Power Analysis (SPA) is a technique that directly analyzes the power consumption information collected during the execution of cryptographic algorithms. It is primarily based on the fact that different key values in public key cryptosystems (PKC) correspond to distinct operations, reflected in the power traces, allowing for key recovery. Effective segmentation of the power trace significantly enhances the efficiency of SPA, reducing the difficulty of key retrieval. This paper introduces a semi-automated Cross-Correlation Based Trace Segmentation method. We experimentally validated the segmentation method in scenarios involving smart cards, USB keys, and microcontrollers simulating unmanned aerial vehicle cryptographic modules. The results demonstrate the method’s high effectiveness in segmenting power traces of PKC.

Original language	English
Title of host publication	Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings
Editors	Martin Andreoni
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	359-375
Number of pages	17
ISBN (Print)	9783031614859
DOIs	https://doi.org/10.1007/978-3-031-61486-6_21
Publication status	Published - 2024
Event	Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024 - Abu Dhabi, United Arab Emirates Duration: 5 Mar 2024 → 8 Mar 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14586 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	5/03/24 → 8/03/24

Keywords

Power trace segmentation
Public key cryptosystems
Side-channel analysis
Simple power analysis

Access to Document

10.1007/978-3-031-61486-6_21

Cite this

Hu, Y., Wang, A., Gong, W., Wu, J., Wang, Z., Zhang, S., & Ma, S. (2024). Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems. In M. Andreoni (Ed.), Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings (pp. 359-375). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14586 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-61486-6_21

Hu, Yaoyuan ; Wang, An ; Gong, Weiping et al. / Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems. Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings. editor / Martin Andreoni. Springer Science and Business Media Deutschland GmbH, 2024. pp. 359-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Simple Power Analysis (SPA) is a technique that directly analyzes the power consumption information collected during the execution of cryptographic algorithms. It is primarily based on the fact that different key values in public key cryptosystems (PKC) correspond to distinct operations, reflected in the power traces, allowing for key recovery. Effective segmentation of the power trace significantly enhances the efficiency of SPA, reducing the difficulty of key retrieval. This paper introduces a semi-automated Cross-Correlation Based Trace Segmentation method. We experimentally validated the segmentation method in scenarios involving smart cards, USB keys, and microcontrollers simulating unmanned aerial vehicle cryptographic modules. The results demonstrate the method{\textquoteright}s high effectiveness in segmenting power traces of PKC.",

keywords = "Power trace segmentation, Public key cryptosystems, Side-channel analysis, Simple power analysis",

author = "Yaoyuan Hu and An Wang and Weiping Gong and Jingjie Wu and Ziyu Wang and Shiming Zhang and Shufan Ma",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024 ; Conference date: 05-03-2024 Through 08-03-2024",

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Hu, Y, Wang, A, Gong, W, Wu, J, Wang, Z, Zhang, S & Ma, S 2024, Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems. in M Andreoni (ed.), Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14586 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 359-375, Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024, Abu Dhabi, United Arab Emirates, 5/03/24. https://doi.org/10.1007/978-3-031-61486-6_21

Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems. / Hu, Yaoyuan; Wang, An; Gong, Weiping et al.
Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings. ed. / Martin Andreoni. Springer Science and Business Media Deutschland GmbH, 2024. p. 359-375 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14586 LNCS).

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

TY - GEN

T1 - Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems

AU - Hu, Yaoyuan

AU - Wang, An

AU - Gong, Weiping

AU - Wu, Jingjie

AU - Wang, Ziyu

AU - Zhang, Shiming

AU - Ma, Shufan

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Simple Power Analysis (SPA) is a technique that directly analyzes the power consumption information collected during the execution of cryptographic algorithms. It is primarily based on the fact that different key values in public key cryptosystems (PKC) correspond to distinct operations, reflected in the power traces, allowing for key recovery. Effective segmentation of the power trace significantly enhances the efficiency of SPA, reducing the difficulty of key retrieval. This paper introduces a semi-automated Cross-Correlation Based Trace Segmentation method. We experimentally validated the segmentation method in scenarios involving smart cards, USB keys, and microcontrollers simulating unmanned aerial vehicle cryptographic modules. The results demonstrate the method’s high effectiveness in segmenting power traces of PKC.

AB - Simple Power Analysis (SPA) is a technique that directly analyzes the power consumption information collected during the execution of cryptographic algorithms. It is primarily based on the fact that different key values in public key cryptosystems (PKC) correspond to distinct operations, reflected in the power traces, allowing for key recovery. Effective segmentation of the power trace significantly enhances the efficiency of SPA, reducing the difficulty of key retrieval. This paper introduces a semi-automated Cross-Correlation Based Trace Segmentation method. We experimentally validated the segmentation method in scenarios involving smart cards, USB keys, and microcontrollers simulating unmanned aerial vehicle cryptographic modules. The results demonstrate the method’s high effectiveness in segmenting power traces of PKC.

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SN - 9783031614859

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

T2 - Satellite Workshops held in parallel with the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024

Y2 - 5 March 2024 through 8 March 2024

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Hu Y, Wang A, Gong W, Wu J, Wang Z, Zhang S et al. Cross-Correlation Based Trace Segmentation for Clustering Power Analysis on Public Key Cryptosystems. In Andreoni M, editor, Applied Cryptography and Network Security Workshops - ACNS 2024 Satellite Workshops, AIBlock, AIHWS, AIoTS, SCI, AAC, SiMLA, LLE, and CIMSS, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 359-375. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-61486-6_21