Optimized Password Recovery for Encrypted RAR on GPUs
This work addresses the time-consuming problem of password recovery for encrypted RAR files, offering a significant speed improvement for security and forensic applications, though it is incremental as it builds on existing GPU methods.
The paper tackled the slow brute-force password recovery for encrypted RAR files by developing a GPU-based approach with coarse-grained parallelism and optimizations like asynchronous CPU-GPU processing, achieving a 43-57 times speedup compared to an optimized CPU version.
RAR uses classic symmetric encryption algorithm SHA-1 hashing and AES algorithm for encryption, and the only method of password recovery is brute force, which is very time-consuming. In this paper, we present an approach using GPUs to speed up the password recovery process. However, because the major calculation and time-consuming part, SHA-1 hashing, is hard to be parallelized, so this paper adopts coarse granularity parallel. That is, one GPU thread is responsible for the validation of one password. We mainly use three optimization methods to optimize this parallel version: asynchronous parallel between CPU and GPU, redundant calculations and conditional statements reduction, and the usage of registers optimization. Experiment result shows that the final version reaches 43~57 times speedup on an AMD FirePro W8000 GPU, compared to a well-optimized serial version on Intel Core i5 CPU.