Information Theory-based Evolution of Neural Networks for Side-channel Analysis
Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ruhr-Universität Bochum
2022-11-01
|
| Series: | Transactions on Cryptographic Hardware and Embedded Systems |
| Subjects: | |
| Online Access: | https://tches.iacr.org/index.php/TCHES/article/view/9957 |
| _version_ | 1797985502060609536 |
|---|---|
| author | Rabin Y. Acharya Fatemeh Ganji Domenic Forte |
| author_facet | Rabin Y. Acharya Fatemeh Ganji Domenic Forte |
| author_sort | Rabin Y. Acharya |
| collection | DOAJ |
| description |
Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN’s hyperparameters, when to stop the training, etc. Our proposed approach, “InfoNEAT,” tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT’s models are robust against noise and desynchronization in traces.
|
| first_indexed | 2024-04-11T07:18:10Z |
| format | Article |
| id | doaj.art-c664c21f5f0b4be8a1eec092dcc9e2b2 |
| institution | Directory Open Access Journal |
| issn | 2569-2925 |
| language | English |
| last_indexed | 2024-04-11T07:18:10Z |
| publishDate | 2022-11-01 |
| publisher | Ruhr-Universität Bochum |
| record_format | Article |
| series | Transactions on Cryptographic Hardware and Embedded Systems |
| spelling | doaj.art-c664c21f5f0b4be8a1eec092dcc9e2b22022-12-22T04:37:51ZengRuhr-Universität BochumTransactions on Cryptographic Hardware and Embedded Systems2569-29252022-11-012023110.46586/tches.v2023.i1.401-437Information Theory-based Evolution of Neural Networks for Side-channel AnalysisRabin Y. Acharya0Fatemeh Ganji1Domenic Forte2University of Florida, Gainesville, USAWorcester Polytechnic Institute, Worcester, USAUniversity of Florida, Gainesville, USA Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those cryptocores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN’s hyperparameters, when to stop the training, etc. Our proposed approach, “InfoNEAT,” tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT’s models are robust against noise and desynchronization in traces. https://tches.iacr.org/index.php/TCHES/article/view/9957Side-channel AnalysisNeural NetworksMulti-layer PerceptronsEvolutionary StrategiesStackingInformation Theory |
| spellingShingle | Rabin Y. Acharya Fatemeh Ganji Domenic Forte Information Theory-based Evolution of Neural Networks for Side-channel Analysis Transactions on Cryptographic Hardware and Embedded Systems Side-channel Analysis Neural Networks Multi-layer Perceptrons Evolutionary Strategies Stacking Information Theory |
| title | Information Theory-based Evolution of Neural Networks for Side-channel Analysis |
| title_full | Information Theory-based Evolution of Neural Networks for Side-channel Analysis |
| title_fullStr | Information Theory-based Evolution of Neural Networks for Side-channel Analysis |
| title_full_unstemmed | Information Theory-based Evolution of Neural Networks for Side-channel Analysis |
| title_short | Information Theory-based Evolution of Neural Networks for Side-channel Analysis |
| title_sort | information theory based evolution of neural networks for side channel analysis |
| topic | Side-channel Analysis Neural Networks Multi-layer Perceptrons Evolutionary Strategies Stacking Information Theory |
| url | https://tches.iacr.org/index.php/TCHES/article/view/9957 |
| work_keys_str_mv | AT rabinyacharya informationtheorybasedevolutionofneuralnetworksforsidechannelanalysis AT fatemehganji informationtheorybasedevolutionofneuralnetworksforsidechannelanalysis AT domenicforte informationtheorybasedevolutionofneuralnetworksforsidechannelanalysis |