Deep Learning Multi-Channel Fusion Attack Against Side-Channel Protected Hardware

Hettwer, Benjamin; Fennes, Daniel; Leger, Sebastien; Richter-Brockmann, Jan; Gehrer, Stefan; Güneysu, Tim

doi:10.1109/dac18072.2020.9218705

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…The works of S. Picek [13] and Hettwer et al [12] are two examples of research that have previously enumerated a number of machine learning-based side-channel attacks. These attacks have been described in a number of other similar studies.…”

Section: Introductionmentioning

confidence: 99%

Design of Deep Learning Technique Based Side Channel Attack Analysis for System on Chips

Fattah

2024

IJPS

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The effectiveness of deep learning techniques has increased in recent years, making it possible to assess side channel attacks on System-on-Chips (SoCs). Specifically, this is due to the fact that they provide a sophisticated method to capitalise on the unintended loss of information that occurs during cryptographic procedures. In this particular scenario, it is very necessary to collect information on side channels, such as power consumption or electromagnetic emissions. There is a procedure called as preprocessing that involves cleaning and modifying the raw data in order to make it more acceptable for input into neural networks. The specifics of the side channel information are what define whether or not a deep learning architecture, such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, or other specialist structures, should be used. Following this, the architecture of the model is painstakingly created, consisting of layers, units, and activation functions, with the purpose of effectively collecting and deciphering the intricate patterns that are associated with the processing of sensitive data on the SOC.As part of this study, we presented a CNN-dependent profiled SCA assault that was directed against an AES cypher that was operating on a SoC. During the phase of setup, we focus on the discharge from a minor capacitor that is linked with the principal line of power supply. This capacitor produces a distinct signal, and we do not capture electromagnetic emission of chip surface. When it comes to the matching and profiling phases, CNN is then used. In light of the fact that the most recent AES round did not uncover any leaks, it is recommended that a novel approach be used in order to retrieve the keys from the first round, Through the use of AES algorithm observations, we were able to develop a differential equation that included the selected intermediate value and basic text. This equation in the end resulted in possibility of advantageous for incorrect value.

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Section: Introductionmentioning

confidence: 99%