Random Forest Profiling Attack on Advanced Encryption Standard

Author(s)

Hiren J. Patel, Air Force Institute of Technology
Rusty O. Baldwin, University of DaytonFollow

Document Type

Article

Publication Date

6-2014

Publication Source

International Journal of Applied Cryptography

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.

Inclusive pages

181-194

ISBN/ISSN

1753-0563

Comments

Permission documentation on file.

Copyright

Copyright © 2014, Inderscience

Publisher

Inderscience

Volume

3

Peer Reviewed

yes

Issue

2

eCommons Citation

Patel, Hiren J. and Baldwin, Rusty O., "Random Forest Profiling Attack on Advanced Encryption Standard" (2014). Computer Science Faculty Publications. 106.
https://ecommons.udayton.edu/cps_fac_pub/106