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http://dx.doi.org/10.13089/JKIISC.2020.30.4.527

Implementation of Instruction-Level Disassembler Based on Power Consumption Traces Using CNN  

Bae, Daehyeon (Hoseo University)
Ha, Jaecheol (Hoseo University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.30, no.4, 2020 , pp. 527-536 More about this Journal
Abstract
It has been found that an attacker can extract the secret key embedded in a security device and recover the operation instruction using power consumption traces which are some kind of side channel information. Many profiling-based side channel attacks based on a deep learning model such as MLP(Multi-Layer Perceptron) method are recently researched. In this paper, we implemented a disassembler for operation instruction set used in the micro-controller AVR XMEGA128-D4. After measuring the template traces on each instruction, we automatically made the pre-processing process and classified the operation instruction set using a deep learning model CNN. As an experimental result, we showed that all instructions are classified with 87.5% accuracy and some core instructions used frequently in device operation are with 99.6% respectively.
Keywords
Side-Channel Attack; Power Analysis; Deep Learning; Convolutional Neural Network(CNN); Disassembler;
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