Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147252
Title: SNR-centric power trace extractors for side-channel attacks
Authors: Ou, Changhai
Lam, Siew-Kei
Sun, Degang
Zhou, Xinping
Qiao, Kevin
Wang, Qu
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Ou, C., Lam, S., Sun, D., Zhou, X., Qiao, K. & Wang, Q. (2021). SNR-centric power trace extractors for side-channel attacks. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems, 40(4), 620-632. https://dx.doi.org/10.1109/TCAD.2020.3003849
Project: NRF2016NCR-NCR001-006
Journal: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Abstract: Existing power trace extractors consider the case where the number of power traces available to the attacker is sufficient to guarantee successful attacks, and the goal of power trace extraction is to extract a small part of traces with high Signal-to-Noise Ratio (SNR) to reduce the complexity of attacks rather than to increase the success rates. Although strict theoretical proofs are given, the existing power trace extractors are too simple and leakage characteristics of Points-Of-Interest (POIs) have not been thoroughly analyzed. They only maximize the variance of data-dependent power consumption component and ignore the noise component, which results in very limited SNR that hampers the performance of extractors. In this paper, we provide a rigorous theoretical analysis of SNR of power traces, and propose a simple yet efficient SNR-centric extractor, named Shortest Distance First (SDF), to extract power traces with the smallest estimated noise by taking advantage of known plaintexts. In addition, to maximize the variance of the exploitable component while minimizing the noise, we refer to the SNR estimation model and propose another novel extractor named Maximizing Estimated SNR First (MESF). Finally, we further propose an advanced extractor called Mean-optimized MESF (MMESF) that exploits the mean power consumption of each plaintext byte value to more accurately and reasonably estimate the data-dependent power consumption of the corresponding samples. Experiments on both simulated power traces and measurements from an ATmega328p micro-controller demonstrate the superiority of our new extractors.
URI: https://hdl.handle.net/10356/147252
ISSN: 0278-0070
DOI: 10.1109/TCAD.2020.3003849
Rights: © 2020 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/TCAD.2020.3003849
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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