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Abstract

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

Ahmed Imran Fattah

Department of Computer Techniques Engineering, Al-Kadhum College (IKC), Baghdad, Iraq

Mohammed Saeb Nahi

Department of Computer Techniques Engineering, Al-Kadhum College (IKC), Baghdad, Iraq

Hassan Jameel Mutashar

Department of Computer Techniques Engineering, Al-Kadhum College (IKC), Baghdad, Iraq

63 - 73
Vol.17, Jan-Jun, 2024
Receiving Date: 2023-12-17
Acceptance Date: 2024-02-13
Publication Date: 2024-02-26
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http://doi.org/10.37648/ijps.v17i01.006

Abstract

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.


Keywords: Deep Learning; Side Channel Analysis; Attacks; System on Chip and AES


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