{"title":"STM32单片机中随机数发生器的侧信道分析","authors":"Kalle Ngo, E. Dubrova","doi":"10.1145/3526241.3530324","DOIUrl":null,"url":null,"abstract":"The hardware random number generator (RNG) integrated in STM32 MCUs is intended to ensure that the numbers it generates cannot be guessed with a probability higher than a random guess. The RNG is based on several ring oscillators whose outputs are combined and post-processed to produce a 32-bit random number per round of computation. In this paper, we show that it is possible to train a neural network capable of recovering the Hamming weight of these random numbers from power traces with a higher than 60% probability. This is a 4-fold improvement over the 14% probability of the most likely Hamming weight.","PeriodicalId":188228,"journal":{"name":"Proceedings of the Great Lakes Symposium on VLSI 2022","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Side-Channel Analysis of the Random Number Generator in STM32 MCUs\",\"authors\":\"Kalle Ngo, E. Dubrova\",\"doi\":\"10.1145/3526241.3530324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The hardware random number generator (RNG) integrated in STM32 MCUs is intended to ensure that the numbers it generates cannot be guessed with a probability higher than a random guess. The RNG is based on several ring oscillators whose outputs are combined and post-processed to produce a 32-bit random number per round of computation. In this paper, we show that it is possible to train a neural network capable of recovering the Hamming weight of these random numbers from power traces with a higher than 60% probability. This is a 4-fold improvement over the 14% probability of the most likely Hamming weight.\",\"PeriodicalId\":188228,\"journal\":{\"name\":\"Proceedings of the Great Lakes Symposium on VLSI 2022\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Great Lakes Symposium on VLSI 2022\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3526241.3530324\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Great Lakes Symposium on VLSI 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3526241.3530324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Side-Channel Analysis of the Random Number Generator in STM32 MCUs
The hardware random number generator (RNG) integrated in STM32 MCUs is intended to ensure that the numbers it generates cannot be guessed with a probability higher than a random guess. The RNG is based on several ring oscillators whose outputs are combined and post-processed to produce a 32-bit random number per round of computation. In this paper, we show that it is possible to train a neural network capable of recovering the Hamming weight of these random numbers from power traces with a higher than 60% probability. This is a 4-fold improvement over the 14% probability of the most likely Hamming weight.
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