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Random Forest profiling attack on advanced encryption standard

Random Forest profiling attack on advanced encryption standard Random Forest, a non-parametric classifier, is proposed for byte-wise profiling attack on advanced encryption standard (AES) and shown to improve results on PIC microcontrollers, especially in high-dimensional variable spaces. It is shown in this research that data collected from 40 PIC microcontrollers exhibited highly non-Gaussian variables. For the full-dimensional dataset consisting of 50,000 variables, Random Forest correctly extracted all 16 bytes of the AES key. For a reduced set of 2,700 variables captured during the first round of the encryption, Random Forest achieved success rates as high as 100% for cross-device attacks on 40 PIC microcontrollers from four different device families. With further dimensionality reduction, Random Forest still outperformed classical template attack for this dataset, requiring fewer traces and achieving higher success rates with lower misclassification rate. The importance of analysing the system noise in choosing a classifier for profiling attack is examined and demonstrated through this work. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Applied Cryptography Inderscience Publishers

Random Forest profiling attack on advanced encryption standard

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1753-0563
eISSN
1753-0571
DOI
10.1504/IJACT.2014.062740
Publisher site
See Article on Publisher Site

Abstract

Random Forest, a non-parametric classifier, is proposed for byte-wise profiling attack on advanced encryption standard (AES) and shown to improve results on PIC microcontrollers, especially in high-dimensional variable spaces. It is shown in this research that data collected from 40 PIC microcontrollers exhibited highly non-Gaussian variables. For the full-dimensional dataset consisting of 50,000 variables, Random Forest correctly extracted all 16 bytes of the AES key. For a reduced set of 2,700 variables captured during the first round of the encryption, Random Forest achieved success rates as high as 100% for cross-device attacks on 40 PIC microcontrollers from four different device families. With further dimensionality reduction, Random Forest still outperformed classical template attack for this dataset, requiring fewer traces and achieving higher success rates with lower misclassification rate. The importance of analysing the system noise in choosing a classifier for profiling attack is examined and demonstrated through this work.

Journal

International Journal of Applied Cryptography – Inderscience Publishers

Published: Jan 1, 2014

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