Abstract
Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic algorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a appropriate preprocess step for each leakage interval. This degenerates the quality of profiling traces due to the noise and non-primary components. In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately. Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model. We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.
Highlights
S IDE Channel Attacks (SCAs) [1] were proposed 20 years ago, and have become a realistic concern recently with the help of deep-learning techniques
We find that in software implementations of Advanced Encryption Standard (AES)-128, a chosen attack point can lead to multiple leakage intervals in the traces
Because the model trained on leakage interval F cannot achieve a classification accuracy larger than 0.39% on the testing set, this interval will will not be involved in the training of the multi-input models
Summary
S IDE Channel Attacks (SCAs) [1] were proposed 20 years ago, and have become a realistic concern recently with the help of deep-learning techniques. Most existing deep learning side channel attacks train models on traces which contain all leakage points directly or mainly focus on the main leakage point. We find that in software implementations of AES-128, a chosen attack point can lead to multiple leakage intervals in the traces. We propose a multi-input deep-learning model in which multiple leakage intervals could be used collaboratively to perform the attack. A leakage function is used to obtain the value related to an attack point based on a specific power model to describe the leakage. The leakage function for multiple attack points is unified as VF _leak 2 This is the first step in building a multi-input model, which is described below.
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