Selection of handwritten signature dynamic indicators for user authentication

Authors

  • Viktor Yevetskyi Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv, http://orcid.org/0000-0002-5364-8076
  • Ivan Horniichuk Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv, http://orcid.org/0000-0001-6754-4764

DOI:

https://doi.org/10.20535/2411-1031.2020.8.1.217994

Keywords:

biometric user authentication, biometric indicator, dynamic indicator, biometric vector, handwritten signature, biometric authentication system

Abstract

Selection and evaluation of handwritten signature indicators in user authentication systems are considered. An important and unresolved issue of information security is the effective identification of the user who accesses confidential information. Traditional password protection has a number of disadvantages. As an alternative to the password system or its addition, user identification by biometric characteristics is considered. An identifier that uses biometric characteristics is inextricably linked to the user and it is almost impossible to use it without authorization. The decision about the truth of the user of biometric authentication systems is made on the basis of comparison of his template with the data entered when trying to authenticate. The template is formed on the basis of studying the selected individual characteristics of the user. To do this, we use the biometric characteristics of users, which reflect the dynamic, behavioral characteristics of the person. It is proposed to use a handwritten signature as a biometric characteristic of the user. A handwritten signature is a socially and legally recognized biometric characteristic used for human authentication. It has a rather complex structure and high detail - all this makes solving the problem of user identification by mathematical methods quite complex and requires high computational costs. Another significant disadvantage is that handwritten authentication systems require the installation of additional specialized equipment, which makes the use of such systems as an ordinary means of authentication very expensive. To solve this problem, a scheme for implementing a computer data protection system against unauthorized access based on handwritten signatures using mobile devices based on the Android operating system as signature input devices is proposed. A method of biometric vector generation for use in dynamic biometric user authentication systems is proposed. The optimal characteristics are investigated and the efficiency of using the proposed form biometric characteristics vector is estimated. Certificates of copyrights registration for software applications developed during the work were received.

Author Biographies

Viktor Yevetskyi, Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv,

candidate of technical sciences,
associate professor, associate professor
at the cybersecurity and application
of information systems and technologies
academic department

Ivan Horniichuk, Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv,

graduate student

References

E. Anisimova, “About the verification problem using handwritten signatures”, Modern technology and technology, no. 3, 2016. [Online]. Available: http://technology.snauka.ru/ 2016/03/9715. Accessed on: Jan. 15, 2020.

A. Skorodumov, “Pros and cons of biometric identification”, Information Security, no. 6, pp. 31-33, 2018. [Online]. Available: http://lib.itsec.ru/articles2. Accessed on: Jan. 20, 2020.

I. Horniichuk, and V. Yevetskiy, “Use of keyboard handwriting in user authentication systems”, Information Technology and Security, vol. 4, iss. 1, pp. 27-33, 2016, doi: https://doi.org/10.20535/2411-1031.2016.4.1.95927.

L. Irwin, “GDPR: Things to consider when processing biometric data”, IT Governance European Blog, 2017. [Online]. Available: https://www.itgovernance.eu/blog/en/gdpr-things-to-consider-when-processing-biometric-data. Accessed on: Dec. 12, 2019.

I. Smirnov, and S. Borisov, “Handwriting recognition when authenticating PC users”, Succeeding in modern natural science, no. 6, pp. 99-100, 2012.

Y. Zheludov, “Identification problems in handwritten recognition systems”, Scientific journal “Informatics”, no. 9 (32), 2018. [Online]. Available: https://cyberleninka.ru/article/n/problemy-identifikatsii-v-sistemah-raspoznavaniya-rukopisnyh-podpisey. Accessed on: Jan. 20, 2020.

Wacom Inc. technology for Developers. Wacom Inc., 2020. [Online]. Available: https://developer.wacom.com/en-us. Accessed on: Marth 01, 2020.

Solutions: HUION. Shenzhen Huion Animation Technology Co., 2020. [Online]. Available: https://support.huion.com/support/solutions. Accessed on: Marth 01, 2020.

Download Developer Tools (API/SDK). Signotec GmbH, 2020. [Online]. Available: https://en.signotec.com/service/downloads/developer-tools-api-sdk-/. Accessed on: Marth 01, 2020.

SignToLogin – Products. Signtologin.com, 2020. [Online]. Available: https://signtologin.com/products. Accessed on: Marth 01, 2020.

DocuSign eSignature. DocuSign Inc., 2020. [Online]. Available: https://www.docusign.com/ products/electronic-signature. Accessed on: Marth 01, 2020.

M™ Electronic Signature Authentication (ESA) Software. 3M.com, 2020. [Online]. Available: https://www.3m.com/3M/en_US/company-us/all-3m-products/~/3M-Electronic-Signature-Authentication-ESA-Software/?N=5002385+3290603306&rt=rud. Accessed on: Marth 01, 2020.

I. Anikin, and E. Anisimova, “Detection of dynamic handwritten signature based on fuzzy logic”, Bulletin of the Kazan State Energy University, no. 3(31), pp. 48-64, 2016.

G. Kozlov, and S. Novikova, “Recognition of handwritten signatures using a wire-line neural network”, in XII International Scientific and Practical Conference. Scientific forum: technical and physical-mathematical sciences, Moscow, 2018, pp. 17-20.

V. Lipsky, “Identification of handwritten signatures using neural networks”, in 54-th scientific conference of post-graduate students, masters and students of BSUIR, Minsk, 2018, pp. 84-85.

I. Horniichuk, V. Yevetskiy, and V. Kubrak, “Applying mobile devices in biometric user authentication systems”, Information Technology and Security, vol. 7, iss. 1, pp. 14-24, 2019, doi: https://doi.org/10.20535/2411-1031.2019.7.1.184213.

David J. C. MacKay, Information Theory, Inference, and Learning Algorithms. Cambridge, UK: Cambridge University Press, 2003.

Danny Thakkar, “False Acceptance Rate (FAR) and False Recognition Rate (FRR) in Biometrics”, Bayometric Blog, 2019. Available: https://www.bayometric.com/false-acceptance-rate-far-false-recognition-rate-frr/. Accessed on: Marth 20, 2020.

E. Ventsel, Theory of probabilities. Moscow, USSR: Nauka, 1969.

I. Horniichuk, and V. Yevetskiy, “Certificate of registration of copyright for a work “Computer program for user registration and authentication by means of the system of authentication of users by their handwritten signature – MarMurAuth Android Module”, Service of Intellectual Property of Ukraine No. 84551, Jan. 18, 2019.

I. Horniichuk, and V. Yevetskiy, “Certificate of registration of copyright for a work “Computer program for user authentication by means of the system of authentication of users by their handwritten signature – MarMurAuth Authentication Module ”, Service of Intellectual Property of Ukraine No. 84552, Jan. 18, 2019.

I. Horniichuk, and V. Yevetskiy, “Certificate of registration of copyright for a work “Computer program for user registration for authentication by means of the system of authentication of users by their handwritten signature – MarMurAuth Registration Module”, Service of Intellectual Property of Ukraine No. 84553, Jan. 18, 2019.

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Published

2020-07-09

How to Cite

Yevetskyi, V., & Horniichuk, I. (2020). Selection of handwritten signature dynamic indicators for user authentication. Information Technology and Security, 8(1), 19–30. https://doi.org/10.20535/2411-1031.2020.8.1.217994

Issue

Section

INFORMATION SECURITY