PDF 버전 1.4-1.6의 CUDA GPU 환경에서 암호 해독 최적 구현
PDF Version 1.4-1.6 Password Cracking in CUDA GPU Environment
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초록
매년 수십만 개의 암호를 분실하거나 잊어버리면서 합법적인 소유자나 권한을 부여받은 법 집행 담당자가 필요한 정보를 사용할 수 없게 된다. 이러한 암호를 되찾기 위해 암호 해독(Password Cracking)이 사용된다. 암호 해독에 CPU 대신 GPU를 사용하면 복구 과정에서 필요한 많은 양의계산을 신속하게 처리할 수 있다. 본 논문은 현재 가장 많이 사용되는 PDF 1.4 –1.6 버전의 암호 해독에 중점을 두고 CUDA를 사용하여 GPU에서최적화한다. MD5 알고리즘의 불필요 연산 제거, RC4 알고리즘의 32비트 워드 통합 구현, 공유메모리 사용의 기법을 사용하였다. 또한 성능향상에영향을 미치는 블록, 스레드 수 탐색을 위해 오토튠 기법을 사용하였다. 결과적으로 RTX 3060, RTX 3090 환경에서 블록 크기 65,536, 스레드크기 96에서 31,460 kp/s(kilo passwords per second), 66,351 kp/s의 처리량을 보였으며, 기존 최고 처리량을 보여주는 해시캣의 처리량보다각각 22.5%, 15.2%를 향상시켰다.
Abstract
Hundreds of thousands of passwords are lost or forgotten every year, making the necessary information unavailable to legitimate ownersor authorized law enforcement personnel. In order to recover such a password, a tool for password cracking is required. Using GPUsinstead of CPUs for password cracking can quickly process the large amount of computation required during the recovery process. Thispaper optimizes on GPUs using CUDA, with a focus on decryption of the currently most popular PDF 1.4–1.6 version. Techniques suchas eliminating unnecessary operations of the MD5 algorithm, implementing 32-bit word integration of the RC4 algorithm, and using sharedmemory were used. In addition, autotune techniques were used to search for the number of blocks and threads that affect performanceimprovement. As a result, we showed throughput of 31,460 kp/s (kilo passwords per second) and 66,351 kp/s at block size 65,536, threadsize 96 in RTX 3060, RTX 3090 environments, and improved throughput by 22.5% and 15.2%, respectively, compared to the crackingtool hashcat that achieves the highest throughput.
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