Data Processing and Applied Technology in the Algorithm and Cracking of NTLM-Hash - A Highly Proficient Parallel Computing Architecture

Jing Cun Bi; Cheng Guang Zhang; Yan Fei Liu; Wen Hui Dou

doi:10.4028/www.scientific.net/AMR.1003.239

Paper Titles

Design and Implementation of an Intelligent Cleaning Robot Based on Fuzzy Control
p.221

Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network
p.226

Research on Applied-Information Technology with Application of Data Mining in Analysis of Consumer Behavior
p.230

Applied Technology in "GNSS if Signal Acquisition System" and Software Receiver Design
p.235

Data Processing and Applied Technology in the Algorithm and Cracking of NTLM-Hash - A Highly Proficient Parallel Computing Architecture
p.239

An Algorithm for Testing the Unreachable Instruction of an Assembly Program in Software Coverage Testing
p.245

Design of Embedded Video Monitoring System Based on DM6437
p.249

Data Processing with an Innovation Self-Adaptive Denoising Amalgamation Algorithm
p.254

The Research of NURBS Curve Interpolation Algorithm Based on Parabolic and the First-Order Taylor Expansion Interpolation Algorithm
p.260

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1003Data Processing and Applied Technology in the...

Data Processing and Applied Technology in the Algorithm and Cracking of NTLM-Hash - A Highly Proficient Parallel Computing Architecture

Abstract:

Compared with CPU, GPU has a stronger parallel architecture and with specific API, the powerful potential contained in GPU can be released to become GPGPU. High computational capability of GPGPU can be used in scientific computation and data processing. This paper focuses on the research of the high frequency collision crack for normal Windows NTLM-Hash by resorting to CUDA API. The result shows that the computation capability of low-end graphic cards could be almost 100 times that of the normal dual-core CPUs, which could greatly lift the efficiency of encryption cracking.

You might also be interested in these eBooks

Advanced Research on Material Engineering, Electrical Engineering and Applied Technology II

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 1003)

Pages:

239-244

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1003.239

Citation:

Cite this paper

Online since:

July 2014

Authors:

Jing Cun Bi*, Cheng Guang Zhang, Yan Fei Liu, Wen Hui Dou

Keywords:

GPGPU, MD4 Crack, NTLM-Hash, Parallel Computation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Zhang Shu, Chu Yanli. CUDA GPU high-performance computing [M]. Peking: China WaterPower Press，(2009).

Google Scholar

[2] Qiu Deyuan. GPGPU Programming Technique—From GLSL, CUDA to OpenCL[M]. Peking: China Machine Press，(2011).

Google Scholar

[3] NVIDIA Corporation. CUDA Programming Guide [EB/OL]. www. nvidia. com，(2009).

Google Scholar

[4] Weng Jie. With the random number MD5 cracking GPU acceleration and optimization of the algorithm [D]. Changsha：National University of Defense Technology，(2010).

Google Scholar