دورية أكاديمية
Deep Learning Method for Power Side-Channel Analysis on Chip Leakages.
العنوان: | Deep Learning Method for Power Side-Channel Analysis on Chip Leakages. |
---|---|
المؤلفون: | Ahmed, Amjed Abbas, Salim, Rana Ali, Hasan, Mohammad Kamrul |
المصدر: | Electronics & Electrical Engineering; 2023, Vol. 29 Issue 6, p50-57, 8p |
مصطلحات موضوعية: | DEEP learning, ADVANCED Encryption Standard, CONVOLUTIONAL neural networks, SIGNAL processing, NOISE control, LEAKAGE |
مستخلص: | Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as maskingcountermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security. [ABSTRACT FROM AUTHOR] |
Copyright of Electronics & Electrical Engineering is the property of Electronics & Electrical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
قاعدة البيانات: | Supplemental Index |
كن أول من يترك تعليقا!