基于余弦测度的Web指纹识别算法的研究与改进

doi:10.11896/jsjkx.180801473

计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 295-398.doi: 10.11896/jsjkx.180801473

所属专题：医学图像

基于余弦测度的Web指纹识别算法的研究与改进

汤文亮¹, 汤树芳², 张平²

(华东交通大学信息工程学院南昌330013)¹
(华东交通大学软件学院南昌330013)²

收稿日期:2018-08-09 修回日期:2018-08-27 出版日期:2019-10-15 发布日期:2019-10-21
通讯作者: 汤文亮(1969-),男,教授,硕士生导师,主要研究方向为信息安全、大数据分析、物联网,E-mail:october12121@163.com。
作者简介:汤树芳(1994-),男,硕士生,主要研究方向为信息安全;张平(1993-),男,硕士生,主要研究方向为大数据分析。
基金资助:
本文受江西省科技支撑项目(20171BBH80005),江西省科技厅项目(2000616078)资助。

Research and Improvement of Web Fingerprint Identification Algorithm Based on Cosine Measure

TANG Wen-liang¹, TANG Shu-fang², ZHANG Ping²

(School of Information Engineering,East China Jiaotong University,Nanchang 330013,China)¹
(School of Software,East China Jiaotong University,Nanchang 330013,China)²

Received:2018-08-09 Revised:2018-08-27 Online:2019-10-15 Published:2019-10-21

摘要/Abstract

摘要： 为了在Web指纹数据库中实现对Web指纹的准确识别,需要对Web指纹识别算法进行研究。采用当前识别算法对Web指纹数据库中的Web指纹进行识别时,识别的结果与实际结果之间存在误差、识别所用的时间较长,因此存在识别准确率低和识别效率低的问题。在余弦测度的基础上提出了一种Web指纹识别算法,在结构特征、静态文件、Cookie设计和关键字4个方面采用源码审计方法完成了对Web指纹的选取,建立了Web指纹数据库。首先提取Web指纹数据库中数据的特征,根据特征提取结果剔除Web指纹数据库中存在的异常数据;然后将余弦距离函数当作相似性度量函数,采用K-means算法对Web指纹数据库中的Web指纹进行聚类;最后根据聚类结果完成对Web指纹的识别。实验结果表明,所提方法可在较短的时间内准确地完成对Web指纹数据库中Web指纹的识别,具有识别准确率高和识别效率高的优点。

关键词: Web指纹, 识别算法, 余弦测度

Abstract: In order to realize the accurate identification of Web fingerprints in the Web fingerprint database,it is necessary to study the Web fingerprint identification algorithm.When the current fingerprint recognition algorithm is used to identify the Web fingerprint in the Web fingerprint database,there is an error between the recognition result and the actual result,and the recognition takes a long time,which result in low recognition accuracy and recognition efficiency.Based on the cosine measure,a Web fingerprint identification algorithm was proposed.The source fingerprint method is used to select the Web fingerprint in the four aspects of structural features,static files,cookie design and keywords,and a Web fingerprint database is established.Firstly,the characteristics of the data in the Web fingerprint database are extracted,and the abnormal data existing in the Web fingerprint database are removed according to the feature extraction result.Then,the cosine distance function is used as the similarity measurement function,and the K-means algorithm is used to cluster the Web fingerprints in the Web fingerprint database.Finally,the identification of the web fingerprint is completed according to the clustering result.The experimental results show that the proposed method can accurately complete the Web fingerprint identification in the Web fingerprint database in a short time,and has the advantages of high recognition accuracy and high recognition efficiency.

Key words: Cosine measure, Recognition algorithm, Web fingerprint

中图分类号:

TP391.41

汤文亮, 汤树芳, 张平. 基于余弦测度的Web指纹识别算法的研究与改进[J]. 计算机科学, 2019, 46(10): 295-398. https://doi.org/10.11896/jsjkx.180801473

TANG Wen-liang, TANG Shu-fang, ZHANG Ping. Research and Improvement of Web Fingerprint Identification Algorithm Based on Cosine Measure[J]. Computer Science, 2019, 46(10): 295-398. https://doi.org/10.11896/jsjkx.180801473

参考文献

[1]CHEN G P.Research on efficient resource mining technology for large Web network data center[J].Modern Electronics Technique,2017,40(24):18-20.(in Chinese)
陈贵平.大型Web网络数据中心资源高效挖掘技术研究[J].现代电子技术,2017,40(24):18-20.
[2]LIU F,ZHANG D,SHEN L.Study on novel Curvature Features for 3D fingerprint recognition[J].Neurocomputing,2015,168(C):599-608.
[3]WANG Y.Accurate Detection of User Characteristic Data in Large Data Network[J].Computer Simulation,2017,34(6):415-418.(in Chinese)
王玥.大数据网络中用户特征数据准确检测仿真[J].计算机仿真,2017,34(6):415-418.
[4]VENKATESH R,MAHESWARI N U,JEYANTHI S.Multiple Criteria Decision Analysis Based Overlapped Latent Fingerprint Recognition System Using fuzzy Sets[J].International Journal of Fuzzy Systems,2018,14(2):1-27.
[5]XIONG P,HU C X,ZHOU X X.Method for quickly identifying zebra crossing by CNN and artificial feature extraction[J].Electronic Design Engineering,2018,26(3):189-193.(in Chinese)
熊平,胡彩霞,周欣星.CNN与人工特征提取快速识别斑马线的方法[J].电子设计工程,2018,26(3):189-193.
[6]DU B Y,WANG M Q,CHEN C F,et al.Tags extraction for Web information based on structure consistency and feature learning[J].Computer Engineering and Applications,2017,53(7):74-78.(in Chinese)
杜博远,王美清,陈长福,等.基于结构一致和特征学习的网页信息标签提取[J].计算机工程与应用,2017,53(7):74-78.
[7]ZHAO D M,LI H.Approach to network security situational ele-ment extraction based on parallel reduction[J].Journal of Computer Applications,2017,37(4):1008-1013.(in Chinese)
赵冬梅,李红.基于并行约简的网络安全态势要素提取方法[J].计算机应用,2017,37(4):1008-1013.
[8]ZHOU X K.A Kind of Data Clustering Algorithm in Wireless Sensor Network[J].Control Engineering of China,2016,23(8):1238-1241.(in Chinese)
周修考.一种用于无线多传感器网络数据聚类算法[J].控制工程,2016,23(8):1238-1241.
[9]XU G Q,ZHOU L R.Research on an Intelligent Algorithm for Public Network Attack Data Mining[J].Computer Measurement & Control,2016,24(10):190-193.(in Chinese)
余国清,周兰蓉.一种公共网络攻击数据挖掘智能算法研究[J].计算机测量与控制,2016,24(10):190-193.
[10]KAUBA C,UHL A.Fingerprint recognition under the influence of image sensor ageing[J].Iet Biometrics,2017,6(4):245-255.
[11]NI Y,LIU J,LIU S,et al.An Indoor Pedestrian Positioning Method Using HMM with a Fuzzy Pattern Recognition Algorithm in a WLAN Fingerprint System[J].Sensors,2016,16(9):1447-1501.
[12]ZHANG Q,YIN Y,YANG G.Unmatched minutiae:Useful information to boost fingerprint recognition[J].Neurocomputing,2016,171(35):1401-1413.
[13]MARASCO E,ROSS A.A Survey on Antispoofing Schemes for Fingerprint Recognition Systems[J].Acm Computing Surveys,2015,47(2):1-36.
[14]XU Z Y,TANG G W,JIANG X L,et al.Research of a hardware-friendly synthetic fingerprint discrimination algorithm[J].Application of Electronic Technique,2016,42(10):54-57.(in Chinese)
徐智勇,唐根伟,姜新泉,等.硬件友好型合成指纹鉴别算法的研究[J].电子技术应用,2016,42(10):54-57.
[15]SHI J P,WU Y Q.A fingerprint minutiae matching algorithm based on chaotic bee colony optimization[J].CAAI Transactions on Intelligent Systems,2016,11(5):613-618.(in Chinese)
史骏鹏,吴一全.基于混沌蜂群优化的指纹匹配算法[J].智能系统学报,2016,11(5):613-618.
[16]ZHAN X S,CAI L Y.Fingerprint Enhancement Algorithm Based on Two-Dimensional Sine Quadric Surface Filter Modulated by Gaussian Function[J].Journal of Data Acquisition & Processing,2017,32(1):62-70.(in Chinese)
詹小四,蔡乐毅.基于高斯调制二维正弦曲面滤波器的指纹增强算法[J].数据采集与处理,2017,32(1):62-70.
[17]GUPTA P,GUPTA P.An accurate fingerprint orientation mo-deling algorithm[J].Applied Mathematical Modelling,2016,40(15／16):7182-7194.
[18]WANG X X,YANG H C.Multiplex Biometric Verification System Based on Fingerprint and Face.Journal of Chongqing University of Technology(Natural Science),2013,27(7):67-70.(in Chinese)
王小雪,杨会成.融合指纹和人脸的生物特征身份认证方法.重庆理工大学学报(自然科学),2013,27(7):67-70.
[19]LIU H Y,MA J H,HUANG Q.Construction method of fingerprint database based on improved Kriging interpolation for indoor location.Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2017,29(6):751-757.(in Chinese)
刘辉元,马金辉,黄琼.基于改进克里金插值的室内定位位置指纹库构建方法.重庆邮电大学学报(自然科学版),2017,29(6):751-757.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于余弦测度的Web指纹识别算法的研究与改进

Research and Improvement of Web Fingerprint Identification Algorithm Based on Cosine Measure

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

Metrics

本文评价

推荐阅读 0

[1]	南世慧, 魏伟, 吴华清, 邹金蓉, 赵志文. 基于KNN和GBDT的Web服务器指纹识别技术 Web Server Fingerprint Identification Technology Based on KNN and GBDT 计算机科学, 2018, 45(8): 141-145. https://doi.org/10.11896/j.issn.1002-137X.2018.08.025
[2]	王汉武, 于涛. 一种改进的自适应多叉树防碰撞算法 Enhanced Adaptive Division Anti-collision Algorithm 计算机科学, 2018, 45(11): 66-69. https://doi.org/10.11896／j.issn.1002-137X.2018.11.008
[3]	李艳萍,姜颖,胡金明,李卫平. 基于曲波变换和余弦测度的人脸识别方法 Face Recognition Method Based on Curvelet Transform and Cosine Rules 计算机科学, 2016, 43(5): 294-297. https://doi.org/10.11896/j.issn.1002-137X.2016.05.056
[4]	. 一种基于模糊评判规则的P2P流识别算法计算机科学, 2008, 35(5): 24-28.
[5]	王源媛何中市. 基于词性探测的中文姓名识别算法计算机科学, 2005, 32(4): 84-86.
[6]	孙沛陈世福. 面向公安部门的人脸识别系统的设计和实现计算机科学, 2004, 31(9): 183-185.
[7]	崔新春张文龙宫剑. 基于小波变换和图像矩特征的人脸识别算法计算机科学, 2002, 29(9): 76-77.