EM-X-DL: Efficient Cross-device Deep Learning Side-channel Attack With Noisy EM Signatures

• Published in: ACM journal on emerging technologies in computing systems Vol. 18; no. 1; pp. 1 - 17
• Main Authors: Danial, Josef, Das, Debayan, Golder, Anupam, Ghosh, Santosh, Raychowdhury, Arijit, Sen, Shreyas
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 01.01.2022
• Subjects: Embedded systems security; Security and privacy; Side-channel analysis and countermeasures; Electromagnetic side-channel attacks; deep learning; end-to-end SCA; cross-device attack; profiling attacks
• ISSN: 1550-4832; 1550-4840
• DOI: 10.1145/3465380

This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA) on AES-128, in the presence of a significantly lower signal-to-noise ratio (SNR) compared to previous works. Using a novel algorithm to intelligently select multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on measurements from the target encryption engine running on an 8-bit Atmel microcontroller. In this way, EM-X-DL achieves >90% single-trace attack accuracy. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with <20 averaged EM traces.