基于击键动力学身份识别的高校网络招生管理系统
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摘要
高校在远程网上录取过程中普遍采用教育部统一开发的远程录取系统。该系统主要完成高校与各省市招生办公室通过互联网的数据服务器连接,共享投档考生信息,并实现考生档案的审阅和录取工作。网上录取操作完成后,高校还应完成大量的后续数据分类与处理工作,但现有教育部录取系统并没有涉及到这些扩展功能模块。为此,本文开发了与教育部的远程录取系统功能相衔接的院校端网络招生管理系统,以便及时高效地完成后续处理工作。
本系统主要包括对各省数据文件的分类存储、数据预处理、新生录取通知书及信封的打印发放、新生信息的分类统计、各省数据汇总维护、新生分班以及新生名册的编制等功能模块。该系统的使用使高校录取工作中与各省招办的远程网络数据操作和本地后续数据处理实现一体化,也使高校内部更好地实现在校学生数据管理平台的统一化,大大提高了高校录取工作的自动化程度和工作效率。
为了更好地保护录取数据,提高系统安全性,本文还重点开发了基于击键动力学的用户身份识别功能模块。该模块在原有的密码保护基础上增加了一层基于生物测定学的保护功能,使得即使密码泄露也能较好地实现安全身份认证。基于系统采集的用户击键特征数据,论文分别采用模糊逻辑和统计学两种算法进行处理,以判定用户的合法身份。在模糊逻辑算法中,作者首次采用了基于黄金分割法的数理统计分类法,将采集数据按照击键速率进行类别划分并设定门限值,取得了较好的效果。
In the process of matriculation, it is normally required to use the network-based matriculation system developed by Moe (Ministry of Education). The system helps the universities to connect with all the provincial data servers, share the candidates information, examine electro-dossiers, and complete the matriculation task. Afterwards, each university should start the subsequent data classification and processing, which is a huge job but the existing system developed by MoE cannot handle it. It is therefore the aim of this thesis to develop an extension system for the university to deal with these tasks efficiently and timely, which can be linked to the existing system by MoE.The system consists of mainly the following modules, classification and backup, data pre-processing, printing and delivering the notification to the accepted students, classified data statistics, data collection and maintenance, students grouping, name list making, and so on. The developed system can process effectively the provincial data and local data in a unified manner, and make the student data be matched coherently with the internal university management information system. In this way, the system enhances significantly the automation degree as well as the working efficiency.In order to better protect student data and to strengthen system security, an identity authentication module based on keystroke dynamics is developed. The module adds an additional biometrics protection to the existing password protection since users can login the system safely even if the password is disclosed. When a user login, the system collects his keystroke characteristics, then process the collected data by based on fuzzy-logic and statistics algorithms. The user's keystroke characteristics can then be figured out, and the user identity can be confirmed. In the fuzzy-logic algorithm, by first using the data statistical taxonomy based on golden section, the collected data are classified into different classes according to the speed of the keystroke and the pre-determined threshold, therefore resulting better identity authentication.
本系统主要包括对各省数据文件的分类存储、数据预处理、新生录取通知书及信封的打印发放、新生信息的分类统计、各省数据汇总维护、新生分班以及新生名册的编制等功能模块。该系统的使用使高校录取工作中与各省招办的远程网络数据操作和本地后续数据处理实现一体化,也使高校内部更好地实现在校学生数据管理平台的统一化,大大提高了高校录取工作的自动化程度和工作效率。
为了更好地保护录取数据,提高系统安全性,本文还重点开发了基于击键动力学的用户身份识别功能模块。该模块在原有的密码保护基础上增加了一层基于生物测定学的保护功能,使得即使密码泄露也能较好地实现安全身份认证。基于系统采集的用户击键特征数据,论文分别采用模糊逻辑和统计学两种算法进行处理,以判定用户的合法身份。在模糊逻辑算法中,作者首次采用了基于黄金分割法的数理统计分类法,将采集数据按照击键速率进行类别划分并设定门限值,取得了较好的效果。
In the process of matriculation, it is normally required to use the network-based matriculation system developed by Moe (Ministry of Education). The system helps the universities to connect with all the provincial data servers, share the candidates information, examine electro-dossiers, and complete the matriculation task. Afterwards, each university should start the subsequent data classification and processing, which is a huge job but the existing system developed by MoE cannot handle it. It is therefore the aim of this thesis to develop an extension system for the university to deal with these tasks efficiently and timely, which can be linked to the existing system by MoE.The system consists of mainly the following modules, classification and backup, data pre-processing, printing and delivering the notification to the accepted students, classified data statistics, data collection and maintenance, students grouping, name list making, and so on. The developed system can process effectively the provincial data and local data in a unified manner, and make the student data be matched coherently with the internal university management information system. In this way, the system enhances significantly the automation degree as well as the working efficiency.In order to better protect student data and to strengthen system security, an identity authentication module based on keystroke dynamics is developed. The module adds an additional biometrics protection to the existing password protection since users can login the system safely even if the password is disclosed. When a user login, the system collects his keystroke characteristics, then process the collected data by based on fuzzy-logic and statistics algorithms. The user's keystroke characteristics can then be figured out, and the user identity can be confirmed. In the fuzzy-logic algorithm, by first using the data statistical taxonomy based on golden section, the collected data are classified into different classes according to the speed of the keystroke and the pre-determined threshold, therefore resulting better identity authentication.
引文
[1] Mantyjarvg J.; Koivumaki, J., Vuori, P. Keystroke recognition for virtual keyboard Multimedia and Expo, 2002. ICME'02. Proceedings. 2002 IEEE International Conference on , Volume: 2, 26-29 Aug. 2002 Page s:429-432 vol.2
[2] Araujo, L.C.E; Sucupira, L.H.R., Jr.; Lizarraga, M.G; Ling, L.L.; Yabu-Uti, J.B.T.; User authentication through typing biometrics featuresSignal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], Volume: 53, Issue: 2, Feb. 2005 Pages:851—855
[3] Peacock, A.; Xian Ke; Wilkerson, M.; Typing patterns: a key to user identification Security & Privacy Magazine, IEEE, Volume: 2, Issue: 5, Sept.-Oct. 2004 Pages:40-47
[4] De Ru, W.G.; Eloff, J.H.P; Enhanced password authentication through fuzzy logic Expert, IEEE [see also IEEE Intelligent Systems] , Volume: 12, Issue: 6, Nov.-Dec. 1997 Pages:38-45
[5] Haider, S.; Abbas, A.; Zaidi, A.K.; A multi-technique approach for user identification through keystroke dynamics Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Volume: 2, 8-11 Oct. 2000 Pages:1336-1341 vol.2
[6] Zhang Changshui; Sun Yanhua; AR model for keystroker verification Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Volume: 4,8-11 Oct. 2000 Pages:2887-2890 vol.4
[7] Bleha, S.A.; Obaidat, M.S.; Dimensionality reduction and feature extraction applications in identifying computer users Systems, Man and Cybernetics, IEEE Transactions on , Volume: 21, Issue: 2, March-April 1991 Pages:452-456
[8] Mandujano, S.; Soto, R.; Deterring password sharing:user authentication via fuzzy c-means clustering applied to keystroke biometric data Computer Science, 2004. ENC 2004. Proceedings of the Fifth Mexican International Conference in, Sept. 20-24, 2004 Pages:181-187
[9] Maisuria, L.K.; Cheng Soon Ong; Wen Kin Lai; A comparison of artificial neural networks and cluster analysis for typing biometrics authentication Neural Networks, 1999. IJCNN'99. International Joint Conference on, Volume: 5,10-16 July 1999 Pages:3295-3299 vol.5
[10] Bleha, S.; Gillespie, D. Computer user identification using the mean and the median as features Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, Volume: 5, 11-14 Oct. 1998 Pages:4379-4381 vol.5
[11] Nassery, P.; Faez, K.;Signature pattern recognition using pseudo Zernike moments and a fuzzy logic classifier Image Processing, 1996. Proceedings., International Conference on , Volume: 1 , 16-19 Sept. 1996 Pages: 197- 200 vol.2
[12] Wen Wei; Mendel, J. M; .A fuzzy classifier that uses both crisp samples and linguistic knowledge Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on , 26-29 June 1994 Pages:792 - 797 vol.2
[13] Budsayaplakorn, R.; Asdornwised, W.; Jitapunkul, S.;On-line Thai handwritten character recognition using hidden Markov model and fuzzy logic Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on , 17-19 Sept. 2003 Pages:537 - 546
[14] Fadhli Wong Mohd Hasan Wong; Supian, A.S.M.; Ismail, A.F.; Lai Weng Kin; Ong Cheng Soon;Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on , Volume: 2 , 4-7 Nov. 2001 Pages:911 -915 vol.2
[15] Shepherd, S.J.;Continuous authentication by analysis of keyboard typing characteristics Security and Detection, 19.Michael Chambers,and Christine L,MacKenzie : Computer User Verificatio95., European Convention on , 16-18 May 1995 Pages:111 - 114
[16] B. Miller. Vital Signs of Identity (Biometrics). IEEE Spectrum, 1994, Vol.31(2): 22-30.
[17]Mohannad S. Obaiat,David T. macchiavolo; An On-Line Neural Network System for Computer Access Security ,IEEE TRANSACTION ON INDUSTRIAL ELECTRONIC ,VOL 40,NO 2,ARPIL 1993 ,235-241
[18]http.7/www.biopassword.com , Biopassword Keystroke Dynamics. Copyright 1999-2001 - Net Nanny Softwear International Inc
[19] Shackelford, A.K.; Davis, C.H.;A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas,Geoscience and Remote Sensing, IEEE Transactions on , Volume: 41 , Issue: 10 , Oct. 2003 ,P2354 - 2363
[20] S.A.Bleha and M.S. Obaidat : Computer Users Verification Using the Perceptron Algorithm. IEEE Transactions on Systems, Man and Cybernetics, 1993, Vol. 23(3): 900-902.
[21 ]Saleh Blcha,Charles Slivinsky,and Bassam Hussien: Computer-Access Security Systemes Using Keystroke Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, Vol. 12(12): 1217-1222.
[22] M.S.Obaidat and Balqies Sadoun: Verification of Computer Users using Keystroke Dynamics. IEEE Transactions on Systems, Man and Cybernetics, 1997, Vol.27(2): 261-269.
[23] Fabian Monrose, Aviel Rubin: Athentication Via Keystroke Dynamics, Proceedings of the Fourth ACM Conference on Computer and Communications Security, 1997, 4: 48-56.
[24] Gentner. Keystroke timing in transcription typing. Cognitive Aspects of skilled typewritting, pages 95{120, 1993.
[25] D. K. Isenor and S.G. Zaky. Fingerprint identi_cation using graph matching.Pattern Recognition, 19(2): 113 {222, 1986.
[26] 曲维光,宋如顺,基于用户击键特征识别的用户认证系统,《计算机工程与应用》2002年第16期,P69-P71。
[27] 李煊 庄镇泉,web访问挖掘预处理的用户识别算法 《计算机工程与应用》 2002年第38卷第7期,P173-P176。
[28] 嘉信泰科,独特的“”击键动力“动态生物识别技术,信息网络安全2003年12期,P60-P61.
[29] 李松涛,校级招生系统的建立,福建电脑,2004年第2期,P48-P49
[30] 古俐明,录取新生信息管理系统的设计与实现,广东民族学院学报(自然科学版),1996年第6期,P62-P64
[31] 陈石清,伊文斌,高校网上招生后续系统的开发与研究,南方冶金学院学报,2003年第9期,P57-P60
[32] 王军,刘俊等,高校网上招生数据处理,沈阳华工学院学报,2004年第3期,P41-P44
[33] 吴智明,刘士平,喻全红,普通高校招生录取管理系统的设计与实现 淮北煤师院学报(自然科学版)2002年6月,第21卷第2期,P23-P27。
[34] 刘学军,陈松灿,彭宏京,基于支持向量机的计算机键盘用户身份验证,计算机研究与发展,2002年9月,第39卷第9期,P1082-P1086。
[35] 薛天宇,一种高效的键盘扫描分析算法。北京服装学院学报(自然科学版),2001年4月第21卷第1期,P31-P35。
[36] 冯登国.关于发展我国信息安全的几点建议.中国科学院院刊,2002年第4期:P289-P291.
[37] 施晓琴,用C语言修改数据库文件,天津成人高等学校联合学报2004年7月第4卷第3期,P107-P110。
[38] 计华,用C语言得到键盘编码的一个例子,山东电子,1998年第2期,P16-P19。
[39] 陈建勇,袁景凌,在VC中动态加载ODBC的方法,武汉理工大学信息工程学院学报,2002年第11期,P174-P174。
[40] 张春生,高校成人教育成绩管理系统的开发,内蒙古民族大学学报(自然科学版),2002年4月,第19卷第2期,P146-P148。
[2] Araujo, L.C.E; Sucupira, L.H.R., Jr.; Lizarraga, M.G; Ling, L.L.; Yabu-Uti, J.B.T.; User authentication through typing biometrics featuresSignal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], Volume: 53, Issue: 2, Feb. 2005 Pages:851—855
[3] Peacock, A.; Xian Ke; Wilkerson, M.; Typing patterns: a key to user identification Security & Privacy Magazine, IEEE, Volume: 2, Issue: 5, Sept.-Oct. 2004 Pages:40-47
[4] De Ru, W.G.; Eloff, J.H.P; Enhanced password authentication through fuzzy logic Expert, IEEE [see also IEEE Intelligent Systems] , Volume: 12, Issue: 6, Nov.-Dec. 1997 Pages:38-45
[5] Haider, S.; Abbas, A.; Zaidi, A.K.; A multi-technique approach for user identification through keystroke dynamics Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Volume: 2, 8-11 Oct. 2000 Pages:1336-1341 vol.2
[6] Zhang Changshui; Sun Yanhua; AR model for keystroker verification Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Volume: 4,8-11 Oct. 2000 Pages:2887-2890 vol.4
[7] Bleha, S.A.; Obaidat, M.S.; Dimensionality reduction and feature extraction applications in identifying computer users Systems, Man and Cybernetics, IEEE Transactions on , Volume: 21, Issue: 2, March-April 1991 Pages:452-456
[8] Mandujano, S.; Soto, R.; Deterring password sharing:user authentication via fuzzy c-means clustering applied to keystroke biometric data Computer Science, 2004. ENC 2004. Proceedings of the Fifth Mexican International Conference in, Sept. 20-24, 2004 Pages:181-187
[9] Maisuria, L.K.; Cheng Soon Ong; Wen Kin Lai; A comparison of artificial neural networks and cluster analysis for typing biometrics authentication Neural Networks, 1999. IJCNN'99. International Joint Conference on, Volume: 5,10-16 July 1999 Pages:3295-3299 vol.5
[10] Bleha, S.; Gillespie, D. Computer user identification using the mean and the median as features Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, Volume: 5, 11-14 Oct. 1998 Pages:4379-4381 vol.5
[11] Nassery, P.; Faez, K.;Signature pattern recognition using pseudo Zernike moments and a fuzzy logic classifier Image Processing, 1996. Proceedings., International Conference on , Volume: 1 , 16-19 Sept. 1996 Pages: 197- 200 vol.2
[12] Wen Wei; Mendel, J. M; .A fuzzy classifier that uses both crisp samples and linguistic knowledge Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on , 26-29 June 1994 Pages:792 - 797 vol.2
[13] Budsayaplakorn, R.; Asdornwised, W.; Jitapunkul, S.;On-line Thai handwritten character recognition using hidden Markov model and fuzzy logic Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on , 17-19 Sept. 2003 Pages:537 - 546
[14] Fadhli Wong Mohd Hasan Wong; Supian, A.S.M.; Ismail, A.F.; Lai Weng Kin; Ong Cheng Soon;Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm Signals, Systems and Computers, 2001. Conference Record of the Thirty-Fifth Asilomar Conference on , Volume: 2 , 4-7 Nov. 2001 Pages:911 -915 vol.2
[15] Shepherd, S.J.;Continuous authentication by analysis of keyboard typing characteristics Security and Detection, 19.Michael Chambers,and Christine L,MacKenzie : Computer User Verificatio95., European Convention on , 16-18 May 1995 Pages:111 - 114
[16] B. Miller. Vital Signs of Identity (Biometrics). IEEE Spectrum, 1994, Vol.31(2): 22-30.
[17]Mohannad S. Obaiat,David T. macchiavolo; An On-Line Neural Network System for Computer Access Security ,IEEE TRANSACTION ON INDUSTRIAL ELECTRONIC ,VOL 40,NO 2,ARPIL 1993 ,235-241
[18]http.7/www.biopassword.com , Biopassword Keystroke Dynamics. Copyright 1999-2001 - Net Nanny Softwear International Inc
[19] Shackelford, A.K.; Davis, C.H.;A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas,Geoscience and Remote Sensing, IEEE Transactions on , Volume: 41 , Issue: 10 , Oct. 2003 ,P2354 - 2363
[20] S.A.Bleha and M.S. Obaidat : Computer Users Verification Using the Perceptron Algorithm. IEEE Transactions on Systems, Man and Cybernetics, 1993, Vol. 23(3): 900-902.
[21 ]Saleh Blcha,Charles Slivinsky,and Bassam Hussien: Computer-Access Security Systemes Using Keystroke Dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, Vol. 12(12): 1217-1222.
[22] M.S.Obaidat and Balqies Sadoun: Verification of Computer Users using Keystroke Dynamics. IEEE Transactions on Systems, Man and Cybernetics, 1997, Vol.27(2): 261-269.
[23] Fabian Monrose, Aviel Rubin: Athentication Via Keystroke Dynamics, Proceedings of the Fourth ACM Conference on Computer and Communications Security, 1997, 4: 48-56.
[24] Gentner. Keystroke timing in transcription typing. Cognitive Aspects of skilled typewritting, pages 95{120, 1993.
[25] D. K. Isenor and S.G. Zaky. Fingerprint identi_cation using graph matching.Pattern Recognition, 19(2): 113 {222, 1986.
[26] 曲维光,宋如顺,基于用户击键特征识别的用户认证系统,《计算机工程与应用》2002年第16期,P69-P71。
[27] 李煊 庄镇泉,web访问挖掘预处理的用户识别算法 《计算机工程与应用》 2002年第38卷第7期,P173-P176。
[28] 嘉信泰科,独特的“”击键动力“动态生物识别技术,信息网络安全2003年12期,P60-P61.
[29] 李松涛,校级招生系统的建立,福建电脑,2004年第2期,P48-P49
[30] 古俐明,录取新生信息管理系统的设计与实现,广东民族学院学报(自然科学版),1996年第6期,P62-P64
[31] 陈石清,伊文斌,高校网上招生后续系统的开发与研究,南方冶金学院学报,2003年第9期,P57-P60
[32] 王军,刘俊等,高校网上招生数据处理,沈阳华工学院学报,2004年第3期,P41-P44
[33] 吴智明,刘士平,喻全红,普通高校招生录取管理系统的设计与实现 淮北煤师院学报(自然科学版)2002年6月,第21卷第2期,P23-P27。
[34] 刘学军,陈松灿,彭宏京,基于支持向量机的计算机键盘用户身份验证,计算机研究与发展,2002年9月,第39卷第9期,P1082-P1086。
[35] 薛天宇,一种高效的键盘扫描分析算法。北京服装学院学报(自然科学版),2001年4月第21卷第1期,P31-P35。
[36] 冯登国.关于发展我国信息安全的几点建议.中国科学院院刊,2002年第4期:P289-P291.
[37] 施晓琴,用C语言修改数据库文件,天津成人高等学校联合学报2004年7月第4卷第3期,P107-P110。
[38] 计华,用C语言得到键盘编码的一个例子,山东电子,1998年第2期,P16-P19。
[39] 陈建勇,袁景凌,在VC中动态加载ODBC的方法,武汉理工大学信息工程学院学报,2002年第11期,P174-P174。
[40] 张春生,高校成人教育成绩管理系统的开发,内蒙古民族大学学报(自然科学版),2002年4月,第19卷第2期,P146-P148。