• Title/Summary/Keyword: 고급 암호 표준

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A White-box ARIA Implementation (화이트박스 ARIA 구현)

  • Hong Tae Kim
    • Convergence Security Journal
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    • v.24 no.1
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    • pp.69-76
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    • 2024
  • The white-box implementation is a cryptographic technique used to protect the secret key of a cryptographic system. It is primarily employed for digital rights management for music and videos. Since 2002, numerous white-box implementations have been developed to ensure secure digital rights management. These have been applied to AES and DES. ARIA, a 128-bit block cipher with an involution substitution and permutation network (SPN), was selected as a South Korean standard in 2004. In this paper, we propose the first white-box ARIA implementation. Our implementation consists of 7,696 lookup tables, with a total size of 1,984 KB. We demonstrate that it also has considerable white-box diversity and white-box ambiguity from a security perspective.

Code Obfuscation using Java Reflection and Exception in Android (안드로이드 환경에서 클래스 반사와 예외 처리를 이용한 임의 코드 수행 방법 및 코드 은닉 방법)

  • Kim, Ji-Yun;Go, Nam-Hyeon;Park, Yong-su
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2014.07a
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    • pp.369-370
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    • 2014
  • 본 논문에서는 안드로이드 환경에서 클래스 반사(Reflection)과 예외처리를 이용하여 안드로이드 보호 시스템을 우회하여 임의의 코드를 수행할 수 있는 방법을 제시한다. 일반적인 자바 환경과는 달리 안드로이드 환경에서는 보안 강화를 위해 APK 파일 내 루트 디렉토리의 클래스 파일만을 반사를 통해 동적 로딩이 가능하다. 하지만, 본 논문에서는 클래스 반사와 예외 처리를 이용하여 임의의 디렉토리 내 파일을 로딩 및 동적 실행할 수 있는 방법을 보이며 이 방법은 저자가 알기로는 기존에 알려지지 않은 방법이다. 이를 기반으로, 본 논문에서는 AES 암호와 동적 로딩을 이용하여, 모바일 어플리케이션의 내부 코드를 은폐하는 기법을 제안한다. 제안기법을 활용 시, 첫째 공격자의 입장에서는 내부 코드를 은폐하여 백신을 우회하는 악성코드 제작이 가능하고, 둘째, 프로그램 제작자의 입장에서는 핵심 알고리즘을 은폐하여 저작권을 보호하는 코드 제작이 가능하다. 안드로이드 버전 4.4.2(Kitkat)에서 프로토타입을 구현하여 제안 기법의 실효성을 보였다.

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Improved real-time power analysis attack using CPA and CNN

  • Kim, Ki-Hwan;Kim, HyunHo;Lee, Hoon Jae
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.1
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    • pp.43-50
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    • 2022
  • 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.

IPC-based Dynamic SM management on GPGPU for Executing AES Algorithm

  • Son, Dong Oh;Choi, Hong Jun;Kim, Cheol Hong
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.2
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    • pp.11-19
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    • 2020
  • Modern GPU can execute general purpose computation on the graphic processing unit, and provide high performance by exploiting many core on GPU. To run AES algorithm efficiently, parallel computational resources are required. However, computational resource of CPU architecture are not enough to cryptographic algorithm such as AES whereas GPU architecture has mass parallel computation resources. Therefore, this paper reduce the time to execute AES by employing parallel computational resource on GPGPU. Unfortunately, AES cannot utilize computational resource on GPGPU since it isn't suitable to GPGPU architecture. In this paper, IPC based dynamic SM management technique are proposed to efficiently execute AES on GPGPU. IPC based dynamic SM management can increase and decrease the number of active SMs by using IPC in run-time. According to simulation results, proposed technique improve the performance by increasing resource utilization compared to baseline GPGPU architecture. The results show that AES improve the performance by 41.2% on average.