Energy-Efficient RAR3 Password Recovery with Dual-Granularity Data Path Strategy

Abstract

Password recovery tools are used to recover lost passwords and regain access to precious data. Due to the extremely large time and energy consumption of password recovery, efficient hardware accelerators are demanded to accelerate the recovery process. However, simple and regular data interconnect paths between data sources and non-blocking hash pipelines are hard to construct for Roshal ARchive version 3 (RAR3) algorithm based on field programmable gate array (FPGA) devices. The difficulty comes from the fact that the message format of the hash pipeline inputs vary with the password length and the secure hash algorithm 1 (SHA-1) iteration phase. To attack this problem, a dual-granularity data path adjustment strategy is proposed to eliminate the randomness of message block formats caused by the irregularity of password length and to efficiently schedule the data through the regular data interconnect paths. Experimental results show that the proposed hardware accelerator for RAR3 password recovery is 3.3 × more energy-efficient than a state-of-the-art implementation Hashcat on NVIDIA GTX 1060 GPU.