Perceived Information Revisited
In this study, we present new analytical metrics for evaluating the performance of side-channel attacks (SCAs) by revisiting the perceived information (PI), which is defined using cross-entropy (CE). PI represents the amount of information utilized by a probability distribution that determines a di...
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| Format: | Article |
| Language: | English |
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Ruhr-Universität Bochum
2022-08-01
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| Series: | Transactions on Cryptographic Hardware and Embedded Systems |
| Subjects: | |
| Online Access: | https://ojs-dev.ub.rub.de/index.php/TCHES/article/view/9819 |
| _version_ | 1827141173504901120 |
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| author | Akira Ito Rei Ueno Naofumi Homma |
| author_facet | Akira Ito Rei Ueno Naofumi Homma |
| author_sort | Akira Ito |
| collection | DOAJ |
| description |
In this study, we present new analytical metrics for evaluating the performance of side-channel attacks (SCAs) by revisiting the perceived information (PI), which is defined using cross-entropy (CE). PI represents the amount of information utilized by a probability distribution that determines a distinguishing rule in SCA. Our analysis partially solves an important open problem in the performance evaluation of deep-learning based SCAs (DL-SCAs) that the relationship between neural network (NN) model evaluation metrics (such as accuracy, loss, and recall) and guessing entropy (GE)/success rate (SR) is unclear. We first theoretically show that the conventional CE/PI is non-calibrated and insufficient for evaluating the SCA performance, as it contains uncertainty in terms of SR. More precisely, we show that an infinite number of probability distributions with different CE/PI can achieve an identical SR. With the above analysis result, we present a modification of CE/PI, named effective CE/PI (ECE/EPI), to eliminate the above uncertainty. The ECE/EPI can be easily calculated for a given probability distribution and dataset, which would be suitable for DL-SCA. Using the ECE/EPI, we can accurately evaluate the SR hrough the validation loss in the training phase, and can measure the generalization of the NN model in terms of SR in the attack phase. We then analyze and discuss the proposed metrics regarding their relationship to SR, conditions of successful attacks for a distinguishing rule with a probability distribution, a statistic/asymptotic aspect, and the order of key ranks in SCA. Finally, we validate the proposed metrics through experimental attacks on masked AES implementations using DL-SCA.
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| first_indexed | 2024-03-12T01:54:17Z |
| format | Article |
| id | doaj.art-734d150fbf9541c58210c7d4eabf7a46 |
| institution | Directory Open Access Journal |
| issn | 2569-2925 |
| language | English |
| last_indexed | 2025-03-20T19:06:49Z |
| publishDate | 2022-08-01 |
| publisher | Ruhr-Universität Bochum |
| record_format | Article |
| series | Transactions on Cryptographic Hardware and Embedded Systems |
| spelling | doaj.art-734d150fbf9541c58210c7d4eabf7a462024-08-23T05:44:46ZengRuhr-Universität BochumTransactions on Cryptographic Hardware and Embedded Systems2569-29252022-08-012022410.46586/tches.v2022.i4.228-254Perceived Information RevisitedAkira Ito0Rei Ueno1Naofumi Homma2NTT Social Informatics Laboratories, Nippon Telegraph and Telephone Corporation, 3–9–11 Midori-cho, Musashino-shi, Tokyo, 180-8535, JapanTohoku University, 2–1–1 Katahira, Aoba-ku, Sendai-shi, Miyagi, 980-8577, JapanTohoku University, 2–1–1 Katahira, Aoba-ku, Sendai-shi, Miyagi, 980-8577, Japan In this study, we present new analytical metrics for evaluating the performance of side-channel attacks (SCAs) by revisiting the perceived information (PI), which is defined using cross-entropy (CE). PI represents the amount of information utilized by a probability distribution that determines a distinguishing rule in SCA. Our analysis partially solves an important open problem in the performance evaluation of deep-learning based SCAs (DL-SCAs) that the relationship between neural network (NN) model evaluation metrics (such as accuracy, loss, and recall) and guessing entropy (GE)/success rate (SR) is unclear. We first theoretically show that the conventional CE/PI is non-calibrated and insufficient for evaluating the SCA performance, as it contains uncertainty in terms of SR. More precisely, we show that an infinite number of probability distributions with different CE/PI can achieve an identical SR. With the above analysis result, we present a modification of CE/PI, named effective CE/PI (ECE/EPI), to eliminate the above uncertainty. The ECE/EPI can be easily calculated for a given probability distribution and dataset, which would be suitable for DL-SCA. Using the ECE/EPI, we can accurately evaluate the SR hrough the validation loss in the training phase, and can measure the generalization of the NN model in terms of SR in the attack phase. We then analyze and discuss the proposed metrics regarding their relationship to SR, conditions of successful attacks for a distinguishing rule with a probability distribution, a statistic/asymptotic aspect, and the order of key ranks in SCA. Finally, we validate the proposed metrics through experimental attacks on masked AES implementations using DL-SCA. https://ojs-dev.ub.rub.de/index.php/TCHES/article/view/9819Side-channel analysisDeep learningOptimal distinguisherSuccess ratePerceived information |
| spellingShingle | Akira Ito Rei Ueno Naofumi Homma Perceived Information Revisited Transactions on Cryptographic Hardware and Embedded Systems Side-channel analysis Deep learning Optimal distinguisher Success rate Perceived information |
| title | Perceived Information Revisited |
| title_full | Perceived Information Revisited |
| title_fullStr | Perceived Information Revisited |
| title_full_unstemmed | Perceived Information Revisited |
| title_short | Perceived Information Revisited |
| title_sort | perceived information revisited |
| topic | Side-channel analysis Deep learning Optimal distinguisher Success rate Perceived information |
| url | https://ojs-dev.ub.rub.de/index.php/TCHES/article/view/9819 |
| work_keys_str_mv | AT akiraito perceivedinformationrevisited AT reiueno perceivedinformationrevisited AT naofumihomma perceivedinformationrevisited |