• DocumentCode
    3094695
  • Title

    Password Recovery for RAR Files Using CUDA

  • Author

    Hu, Guang ; Ma, Jianhua ; Huang, Benxiong

  • Author_Institution
    Dept. of Electron & Inf., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • fYear
    2009
  • fDate
    12-14 Dec. 2009
  • Firstpage
    486
  • Lastpage
    490
  • Abstract
    Driven by the insatiable demand of real-time graphics, especially from the market of computer games, graphics processing unit (GPU) is becoming a major computing horsepower during recent years since the performance of GPU is surpassing that of the contemporary CPU. This paper presents our study on how to efficiently recover the passwords for encrypted RAR files. Our research focus is on the AES key generation processing, which is the most time consuming stage in the whole RAR encryption/decryption process. The design and implementation of the password recovery are based on NVIDIA´s CUDA (computer unified device architecture). A CPU-based version is also implemented as a reference and the performance comparison with that of the GPU-based version. In addition, a modified model is proposed to estimate the performance by static analysis of code for and then further assist program optimization.
  • Keywords
    computer graphic equipment; cryptography; file organisation; AES key generation processing; CPU-based version; GPU-based version; NVIDIA CUDA; RAR encryption-decryption process; RAR files; computer games; computer unified device architecture; graphics processing unit; password recovery; program optimization; Central Processing Unit; Computer architecture; Computer graphics; Coprocessors; Cryptography; Electrons; Hardware; Parallel processing; Performance analysis; Process design; AES; CUDA; GPU; parallel computing; password recovery;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Dependable, Autonomic and Secure Computing, 2009. DASC '09. Eighth IEEE International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3929-4
  • Electronic_ISBN
    978-1-4244-5421-1
  • Type

    conf

  • DOI
    10.1109/DASC.2009.123
  • Filename
    5380390