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Title: A highly efficient power model for Correlation Power Analysis (CPA) of pipelined Advanced Encryption Standard (AES)
Authors: Ng, Jun-Sheng
Chen, Juncheng
Kyaw, Nay Aung
Lwin, Ne Kyaw Zwa
Ho, Weng-Geng
Chong, Kwen-Siong
Gwee, Bah-Hwee
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Ng, J.-S., Chen, J., Kyaw, N. A., Lwin, N. K. Z., Ho, W.-G., Chong, K.-S., & Gwee, B.-H. (2020). A highly efficient power model for Correlation Power Analysis (CPA) of pipelined Advanced Encryption Standard (AES). Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 1-5. doi:10.1109/ISCAS45731.2020.9180778.
Project: NRF2018NCR-NCR002-0001
Abstract: We evaluate the vulnerability of a pipelined Advanced Encryption Standard (AES) against Correlation Power Analysis (CPA) Side-Channel Attack (SCA). We identify that the registers in pipelined AES are most vulnerable against CPA SCA and propose a new power model targeting the switching activities of the registers. The proposed power model is constructed based on the Hamming Distance (HD) between the intermediate values stored in the registers in two consecutive clock cycles. Then, we analyze the vulnerability of pipelined AES under two scenarios. First, during regular pipeline operation where the device is performing AES pipeline operation. Second, in non-pipeline operation where we assume the adversaries can insert delay to the input of the device to increase the signal to noise ratio of the physical leakage information. The simulation results show that under regular pipelined operation, our proposed power model can reveal all the 16 key bytes in less than 4,900 traces, resulting in 4.7× more effective than the conventional power models. Under non-pipelined operation, our proposed power model requires only 590 traces to reveal all the 16 key bytes, which is 5.9× more effective than other power models.
ISSN: 2158-1525
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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