Authors: Hiren Patel; Rusty O. Baldwin
Addresses: Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH 45433, USA ' Cyber Center of Excellence, Riverside Research, 2640 Hibiscus Way, Beavercreek, OH 45431, USA
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.
Keywords: side channel attacks; random forest classifier; profiling attacks; machine learning; security; advanced encryption standard; AES; cryptography; PIC microcontrollers; high-dimensional variable spaces.
International Journal of Applied Cryptography, 2014 Vol.3 No.2, pp.181 - 194
Available online: 11 Jun 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article