Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of th...Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of the oracle may suffer accuracy degradation when instantiating it through a side-channel trace distinguisher due to the environmental noise and the cross-device issue.While various deep learning-based approaches have been proposed to address the inaccuracy problem caused by the cross-device issue,they often suffer from complexity and limited interpretability.This work investigates realistic numerous side-channel attack(SCA)scenarios and focuses on the cross-device issue when implementing a reliable PC oracle in SCAs against Kyber.TtLR is proposed,it combines the ttest with a logistic regression model to implement a lightweight but effcient side-channel distinguisher against Kyber KEM.The proposed approach is validated through experiments on STM32F407G boards equipped with ARM Cortex-M4 microcontrollers,using the Kyber512 implementations from the pqm4 library.The results demonstrate that the proposed method achieves high PC oracle accuracy across different boards with low computational and memory overhead.This makes the proposed distinguisher practical for deployment on resource-constrained platforms such as the Raspberry Pi running a Linux system.展开更多
基金National Natural Science Foundation of China(62172374)。
文摘Plaintext-checking(PC)oracle-based key recovery attack stands out as one of the most critical threat targeting Kyber due to its high effciency and ease of implementation.In practical scenarios,however,the output of the oracle may suffer accuracy degradation when instantiating it through a side-channel trace distinguisher due to the environmental noise and the cross-device issue.While various deep learning-based approaches have been proposed to address the inaccuracy problem caused by the cross-device issue,they often suffer from complexity and limited interpretability.This work investigates realistic numerous side-channel attack(SCA)scenarios and focuses on the cross-device issue when implementing a reliable PC oracle in SCAs against Kyber.TtLR is proposed,it combines the ttest with a logistic regression model to implement a lightweight but effcient side-channel distinguisher against Kyber KEM.The proposed approach is validated through experiments on STM32F407G boards equipped with ARM Cortex-M4 microcontrollers,using the Kyber512 implementations from the pqm4 library.The results demonstrate that the proposed method achieves high PC oracle accuracy across different boards with low computational and memory overhead.This makes the proposed distinguisher practical for deployment on resource-constrained platforms such as the Raspberry Pi running a Linux system.
