Dual-Channel Side Channel Attack: Improved AES Key Decryption by Combining Power and Electromagnetic Side Channels with Convolutional Neural Networks

Author

Sean P. O'Neill, Air Force Institute of Technology

Date of Award

12-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Electrical and Computer Engineering

First Advisor

Richard K. Martin, PhD

Abstract

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the need for tedious feature extraction techniques.

AFIT Designator

AFIT-ENG-DS-24-D-035

Comments

Approved for Public release. Distribution unlimited. PA Case number on file.

A 12-month embargo was observed for posting this dissertation.

Recommended Citation

O'Neill, Sean P., "Dual-Channel Side Channel Attack: Improved AES Key Decryption by Combining Power and Electromagnetic Side Channels with Convolutional Neural Networks" (2024). Theses and Dissertations. 8194.
https://scholar.afit.edu/etd/8194