Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151136
Title: Normalized Differential Power Analysis - for ghost peaks mitigation
Authors: Chen, Juncheng
Ng, Jun-Sheng
Kyaw, Nay Aung
Lwin, Ne Kyaw Zwa
Ho, Weng-Geng
Chong, Kwen-Siong
Lin, Zhiping
Chang, Joseph Sylvester
Gwee, Bah-Hwee
Keywords: Engineering::Computer science and engineering::Hardware
Issue Date: 2021
Source: Chen, J., Ng, J., Kyaw, N. A., Lwin, N. K. Z., Ho, W., Chong, K., Lin, Z., Chang, J. S. & Gwee, B. (2021). Normalized Differential Power Analysis - for ghost peaks mitigation. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). https://dx.doi.org/10.1109/ISCAS51556.2021.9401487
Project: NRF2018NCR-NCR002-001
Abstract: The attack efficacy of Differential Power Analysis (DPA), a popular side channel evaluation technique for key extraction, is compromised by the false highest Difference Of Means (DOMs) value (‘ghost peaks’) in the DOMs matrix produced in a conventional DPA. The ghost peak is generated by the wrong key guess and always occurs in the conventional DPA when the number of side channel traces is not enough. In this paper, an improved version of the conventional DPA termed as Normalized DPA (NDPA) is proposed to circumvent the ghost peak. With the analysis on the generation of ghost peaks in the conventional DPA, we observed that by normalizing the DOMs matrix, the ghost peaks can be greatly suppressed. We model the proposed NDPA mathematically and show that it performs better than the conventional DPA. We further provide the experimental validations on a set of 200k power simulation traces on AES SBox and 500 EM traces from ASCAD dataset. Based on the attack results of these datasets, our proposed NDPA requires (up to 68%) lesser number of traces to reveal a correct key when compared to the conventional DPA.
URI: https://hdl.handle.net/10356/151136
DOI: 10.1109/ISCAS51556.2021.9401487
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ISCAS51556.2021.9401487
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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