Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Abstract

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 preprocessing 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.

Electromagnetic and Power Side-Channel Analysis: Advanced Attacks and Low-Overhead Generic Countermeasures through White-Box Approach

“…where K target is the target key byte, Pr(Majority(N) = K target ) gives the probability of successful target key recovery utilizing majority voting with N traces and p is the single-trace X-DeepSCA attack accuracy. To further enhance the effectiveness of X-DeepSCA attacks, pre-processing techniques such as principal component analysis (PCA) for dimensionality reduction and dynamic time warping (DTW) have been explored [30]. The code for the X-DeepSCA attack is shared publicly in GitHub [31].…”

Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

“…To explore how board diversity affects the attack accuracy of the trained deep-learning models, Wang et al [7] show to extend which a model trained for one device can lead to successful attacks on another device. To mitigate the effect caused by the board diversity, References [8][9][10] propose a cross-device approach, which trains models on traces captured from multiple devices. Besides, in [11], the newly proposed federated learning framework [12] is applied to improve the attack efficiency to break an 8-bit ATx-mega128D4 microcontroller implementation of AES-128.…”

NIST-certified secure key generation via deep learning of physical unclonable functions in silica aerogels

“…Current cryptography methods for addressing security issues center on the idea of having a digital key, which is safely stored and whose information remains unknown to an adversary. However, implementing this simple concept turns out to be a difficult task: software such as Trojan horses and malware, and side-channel attacks carried out by enemies with single access to the device, can expose the key and lead to security breaches [4,[9][10][11][12]. As Tim Cook (Apple CEO) emphasized in a recent interview [13]:…”