CPA와 CNN을 활용한 향상된 실시간 전력 분석 공격
Improved real-time power analysis attack using CPA and CNN
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초록
CPA(Correlation Power Analysis)는 암호 알고리즘이 탑재된 공격 대상 장비의 미세한 소비전력을 측정하여 90% 이상의 확률로 암호 알고리즘에 사용된 비밀키를 추측하는 부채널 공격 방법이다. CPA는 통계를 기반으로 분석을 수행하기 때문에 반드시 많은 양의 데이터가 요구된다. 따라서 CPA는 매회 공격을 위해 약 15분 이상 소비전력을 측정해야만 한다. 본 논문에서는 CPA의 데이터 수집 문제를 해결하기 위해 입력데이터를 축적하고 결과를 예측할 수 있는CNN(Convolutional Neural Network)을 사용하는 방법을 제안한다. 사전에 공격 대상 장비의 소비전력을 수집 및 학습을 통해 임의의 소비전력을 입력시키면 즉각적으로 비밀키를 추정할 수 있어연산속도를 향상하고 96.7%의 비밀키 추측 정확도를 나타냈다.
Abstract
Correlation Power Analysis(CPA) is a sub-channel attack method that measures the detailed power consumption of attack target equipment equipped with cryptographic algorithms and guesses the secret key used in cryptographic algorithms with more than 90% probability. Since CPA performs analysis based on statistics, a large amount of data is necessarily required. Therefore, the CPA must measure power consumption for at least about 15 minutes for each attack. In this paper proposes a method of using a Convolutional Neural Network(CNN) capable of accumulating input data and predicting results to solve the data collection problem of CPA. By collecting and learning the power consumption of the target equipment in advance, entering any power consumption can immediately estimate the secret key, improving the computational speed and 96.7% of the secret key estimation accuracy.
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