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DBLP
Article . 2022
Data sources: DBLP
DBLP
Article . 2023
Data sources: DBLP
ACM Computing Surveys
Article . 2023 . Peer-reviewed
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The Side-channel Metrics Cheat Sheet

Authors: Kostas Papagiannopoulos; Ognjen Glamocanin; Melissa Azouaoui; Dorian Ros; Francesco Regazzoni 0001; Mirjana Stojilovic;

The Side-channel Metrics Cheat Sheet

Abstract

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions and devices. Accordingly, many metrics have been adopted or defined to express and quantify side-channel security. These metrics can relate to one another, but also conflict in terms of effectiveness, assumptions, and security goals. In this work, we review the most commonly used metrics in the field of side-channel analysis. We provide a self-contained presentation of each metric, along with a discussion of its limitations. We practically demonstrate the metrics on examples of relevant implementations of the Advanced Encryption Standard (AES), and make the software implementation of the presented metrics available to the community as open source. This work, being beyond a survey of the current status of metrics, will allow researchers and practitioners to produce a well-informed security evaluation through a better understanding of its supporting and summarizing metrics.

Countries
Netherlands, Switzerland
Keywords

cryptography, information theoretic metrics, side-channel attacks, side-channel analysis metrics, 620

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
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