Tipping the Balance: Imbalanced Classes in Deep-Learning Side-Channel Analysis
Article Properties
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DOI (url)
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Publication Date2024/04/01
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Journal
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Indian UGC (journal)
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Refrences11
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Citations1
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Stjepan Picek Faculty of Science, Radboud University, Nijmegen, GL, The Netherlands ORCID (unauthenticated)
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Annelie Heuser CNRS, Inria, IRISA, University of Rennes, Rennes, France ORCID (unauthenticated)
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Alan Jovic Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia ORCID (unauthenticated)
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Shivam Bhasin Temasek Laboratories, Nanyang Technological University, Jurong West, Singapore ORCID (unauthenticated)
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Francesco Regazzoni Security by Design, University of Amsterdam, Amsterdam, WP, The Netherlands ORCID (unauthenticated)
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis | IACR Transactions on Cryptographic Hardware and Embedded Systems |
| 11 | 2022 |
A Novel Evaluation Metric for Deep Learning-Based Side Channel Analysis and Its Extended Application to Imbalanced Data | IACR Transactions on Cryptographic Hardware and Embedded Systems |
| 12 | 2020 |
The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations | IACR Transactions on Cryptographic Hardware and Embedded Systems |
| 26 | 2018 |
| Template attacks | 2002 | |||
| Power Analysis Attacks: Revealing the Secrets of Smart Cards | 2006 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel Attacks | IEEE Transactions on Consumer Electronics |
| 2024 |
Citations Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel Attacks and was published in 2024. The most recent citation comes from a 2024 study titled Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel Attacks. This article reached its peak citation in 2024, with 1 citations. It has been cited in 1 different journals. Among related journals, the IEEE Transactions on Consumer Electronics cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.