  Power side-channel analysis (SCA) has been of immense interest to most
embedded designers to evaluate the physical security of the system. This work
presents profiling-based cross-device power SCA attacks using deep learning
techniques on 8-bit AVR microcontroller devices running AES-128. Firstly, we
show the practical issues that arise in these profiling-based cross-device
attacks due to significant device-to-device variations. Secondly, we show that
utilizing Principal Component Analysis (PCA) based pre-processing and
multi-device training, a Multi-Layer Perceptron (MLP) based 256-class
classifier can achieve an average accuracy of 99.43% in recovering the first
key byte from all the 30 devices in our data set, even in the presence of
significant inter-device variations. Results show that the designed MLP with
PCA-based pre-processing outperforms a Convolutional Neural Network (CNN) with
4-device training by ~20%in terms of the average test accuracy of cross-device
attack for the aligned traces captured using the ChipWhisperer
hardware.Finally, to extend the practicality of these cross-device attacks,
another pre-processing step, namely, Dynamic Time Warping (DTW) has been
utilized to remove any misalignment among the traces, before performing PCA.
DTW along with PCA followed by the 256-class MLP classifier provides >=10.97%
higher accuracy than the CNN based approach for cross-device attack even in the
presence of up to 50 time-sample misalignments between the traces.
