Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (4): 312-317.doi: 10.11896/jsjkx.190300104

• Information Security • Previous Articles     Next Articles

Design and Implementation of Rule Processor Based on Heterogeneous Computing Platform

CHEN Meng-dong, GUO Dong-sheng, XIE Xiang-hui, WU Dong   

  1. State Key Laboratory of Mathematical Engineering and Advanced Computing,Wuxi,Jiangsu 214125,China
  • Received:2019-03-21 Online:2020-04-15 Published:2020-04-15
  • Contact: XIE Xiang-hui,born in 1958,Ph.D,se-nior engineer,Ph.D supervisor.His main research interests include computer architecture.
  • About author:CHEN Meng-dong,born in 1984,postgraduate,engineer.His main research interests include computer architecture.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61732018).

PDF (PC)

728

Abstract: Using dictionaries and their transformation rules is a common method.In recovering the secure string in the identity authentication mechanism.Through the processing of the transformation rules,a large number of targeted new strings can be quickly generated for verification.The rule processing process is complex,and has high requirements on processing performance and system power consumption.The existing tools and research are processed based on software,which are difficult to meet the needs of the actual recovery system.To this end,a rule processor technology based on heterogeneous computing platform was proposed in this paper.For the first time,reconfigurable FPGA hardware is used to accelerate the process of rule processing.At the same time,the ARM universal computing core is used to configure,manage and monitor the process of rule processing.It is implemented on Xilinx Zynq XC7Z030 chip.The experimental results show that the performance of the rule processor based on the hybrid architecture is 214 times higher than that of the rule processor based on ARM only.Typically,the performance of rule processor is better than that of Intel i7-6700 CPU.Compared with NVIDIA GeForce GTX 1080 Ti GPU,the performance power ratio of rule processor is 1.4-2.1 times higher,70 times higher than that of CPU,which effectively improves the speed and efficiency of rule processing.The experimental data fully show that the speed and efficiency of rule processing can be effectively solved by using hardware-accelerated rule processor based on heterogeneous computing platform,which can meet the actual engineering requirements and provide a basis for the design of the whole secure string recovery system.

Key words: Character string, Heterogeneous, Identity authentication, Processor, Rule

CLC Number: 

  • TP391
[1]GUO X.The Scale-free Network of Passwords:Visualizationand Estimation of Empirical Passwords [EB/OL].[2019-03-18].http://arxiv.org/pdf/1511.08324v1.
[2]WU Z Y,CHIANG D L,LIN T C,et al.A Reliable Dynamic User-Remote Password Authentication Scheme over Insecure Network [C]// International Conference on Advanced Information Networking & Applications Workshops.IEEE,2012.
[3]LIU J.Research and application of password cracking methodbased on special dictionary[D].Harbin:Harbin Institute of Technology,2015.
[4]STEUBE J.hashcat advanved password recovery [EB/OL].[2019-03-18].https://hashcat.net/hashcat/.
[5]Openwall.John the Ripper password cracker [EB/OL].[2019-03-18].http://www.openwall.com/john/doc/.
[6]DAS A,BONNEAU J,CAESAR M,et al.The Tangled Web of Password Reuse[C]// Network and Distributed System Security Symposium.2014.
[7]TATLI E I.Cracking More Password Hashes With Patterns[J].IEEE Transactions on Information Forensics & Security,2015,10(8):1656-1665.
[8]NSAKEY.Password cracking rules and masks for hashcat that igenerated from cracked passwords[EB/OL].https://github:com/NSAKEY/nsa-rules.
[9]GOODIN,D.Anatomy of a hack:How crackers ransack passwords like “qeadzcwrsfxv1331”[EB/OL].http://arstechnica.com/security/2013/05/how-crackers-make-minced-meat-out-of-yourpasswords/.
[10]UR B,SEGRETI S M,BAUER L,et al.Measuring real-worldaccuracies and biases in modeling password guessability[C]// Usenix Conference on Security Symposium.2015.
[11]VERAS R,THORPE J,COLLINS C.Visualizing semantics in passwords:The role of dates[C]// Proceedings of the Ninth International Symposium on Visualization for Cyber Security(VizSec ’12).New York,NY,USA:ACM,2012:88-95.
[12]MELICHER W,UR B,SEGRETI S M,et al.Fast,lean,and accurate:Modeling password guessability using neural networks[C]//25th USENIX Security Symposium (USENIX Security 16).Austin,TX:USENIX Association,2016:175-191.
[13]CHOU H C,LEE H C,YU H J,et al.Password cracking based on learned patterns from disclosed passwords[C]//IJICIC.2013.
[14]FAHL S,HARBACH M,ACAR Y,et al.On the ecological validity of a password study[C]// Symposium on Usable Privacy and Security.2013:1-13.
[15]DE CAMAVALET X C,MANNAN M.From very weak to very strong:Analyzing password—strength meters[C]//Proceedings of the Network and Distributed System Security Symposium.2014.
[16]HUH J H,OH S,KIM H,et al.Surpass:System-initiated Userreplaceable Passwords[C]// The 22nd ACM SIGSAC Confe-rence on Computer and Communications Security.2015:170-181.
[17]MILO F,BERNASCHI M,BISSON M.A fast,GPU based,dictionary attack to OpenPGP secret keyrings[J].Journal of Systems & Software,2011,84(12):2088-2096.
[18]XU L,GE C,QIU W,et al.Password Guessing Based on LSTM Recurrent Neural Networks[C]// IEEE International Confe-rence on Computational Science and Engineering.IEEE,2017:785-788.
[19]Xilinx.Zynq-7000 All Programmable SoC [EB/OL].[2019-03-18].https://www.xilinx.com/support/documentation/pro-duct-briefs/zynq-7000-product-brief.pdf.
[1] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2] HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82.
[3] LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[4] CHEN Jing, WU Ling-ling. Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment [J]. Computer Science, 2022, 49(8): 108-112.
[5] ZHANG Lu-ping, XU Fei. Survey on Spiking Neural P Systems with Rules on Synapses [J]. Computer Science, 2022, 49(8): 217-224.
[6] DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[7] LI Tang, QIN Xiao-lin, CHI He-yu, FEI Ke. Secure Coordination Model for Multiple Unmanned Systems [J]. Computer Science, 2022, 49(7): 332-339.
[8] CAO Yang-chen, ZHU Guo-sheng, SUN Wen-he, WU Shan-chao. Study on Key Technologies of Unknown Network Attack Identification [J]. Computer Science, 2022, 49(6A): 581-587.
[9] CHEN Xin, LI Fang, DING Hai-xin, SUN Wei-ze, LIU Xin, CHEN De-xun, YE Yue-jin, HE Xiang. Parallel Optimization Method of Unstructured-grid Computing in CFD for DomesticHeterogeneous Many-core Architecture [J]. Computer Science, 2022, 49(6): 99-107.
[10] ZHAO Luo-cheng, QU Zhi-hao, XIE Zai-peng. Study on Communication Optimization of Federated Learning in Multi-layer Wireless Edge Environment [J]. Computer Science, 2022, 49(3): 39-45.
[11] ZHANG Geng-qiang, XIE Jun, YANG Zhang-lin. Accelerating Forwarding Rules Issuance with Fast-Deployed-Segment-Routing(FDSR) in SD-MANET [J]. Computer Science, 2022, 49(2): 377-382.
[12] PU Shi, ZHAO Wei-dong. Community Detection Algorithm for Dynamic Academic Network [J]. Computer Science, 2022, 49(1): 89-94.
[13] JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
[14] ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei. Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion [J]. Computer Science, 2021, 48(9): 68-76.
[15] YANG Lin, WANG Yong-jie, ZHANG Jun. FAWA:A Negative Feedback Dynamic Scheduling Algorithm for Heterogeneous Executor [J]. Computer Science, 2021, 48(8): 284-290.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.