Deep Learning-based Attacks on Masked AES Implementation,ERICDATA高等教育知識庫
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篇名
Deep Learning-based Attacks on Masked AES Implementation
並列篇名
Deep Learning-based Attacks on Masked AES Implementation
作者 Daehyeon BaeJongbae HwangJaecheol Ha
英文摘要

To ensure the confidentiality of the message, the AES (Advanced Encryption Standard) block cipher algorithm can be widely used. Furthermore, an implementation of masked AES is often used to resist side-channel attacks. To recover secret keys embedded in cryptographic devices with masked AES, we present some side-channel attacks based on deep learning models in profiling and non-profiling scenarios. The proposed method which applies the mask value profiling technique represents new approaches for extracting the secret key. To defeat the masked AES implementation, deep learning models such as multi-layer perceptron and convolutional neural networks are developed. In a non-profiling scenario, we adopt the DDLA (Differential Deep Learning Analysis) to extract sensitive information such as the secret key. The main idea of our method is that it is possible to adopt a new binary labeling method to conduct the DDLA based on the HW (Hamming Weight) model. We show several experiments using real power traces measured from the ChipWhisperer platform in profiling attacks and the ASCAD dataset in non-profiling attacks respectively. Whether we target naïve or masked AES implementation, the experimental results show the predominant key recovery accuracy.

 

起訖頁 897-902
關鍵詞 Internet of thingsSide-channel attackMasked AES implementationDeep learning
刊名 網際網路技術學刊  
期數 202207 (23:4期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022072304024  複製DOI
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