CNN을 이용한 소비 전력 파형 기반 명령어 수준 역어셈블러 구현
Implementation of Instruction-Level Disassembler Based on Power Consumption Traces Using CNN
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초록
정보보호용 디바이스의 부채널 정보인 소비 전력 파형을 이용하면 내장된 비밀 키 뿐만 아니라 동작 명령어를 복구할 수 있음이 밝혀졌다. 최근에는 MLP 등과 같은 딥러닝 모델을 이용한 프로파일링 기반의 부채널 공격들이 연구되고 있다. 본 논문에서는 마이크로 컨트롤러 AVR XMEGA128-D4가 사용하는 명령어에 대한 역어셈블러를 구현하였다. 명령어에 대한 템플릿 파형을 수집하고 전처리하는 과정을 자동화하였으며 CNN 딥러닝 모델을 사용하여명령-코드를 분류하였다. 실험 결과, 전체 명령어는 약 87.5%의 정확도로, 사용 빈도가 높은 주요 명령어는 99.6%의 정확도로 분류될 수 있음을 확인하였다.
Abstract
It has been found that an attacker can extract the secret key embedded in a security device and recover the operationinstruction using power consumption traces which are some kind of side channel information. Many profiling-based sidechannel attacks based on a deep learning model such as MLP(Multi-Layer Perceptron) method are recently researched. Inthis 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 theoperation instruction set using a deep learning model CNN. As an experimental result, we showed that all instructions areclassified with 87.5% accuracy and some core instructions used frequently in device operation are with 99.6% respectively.
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