Article

Article title IMMUNOLOGICAL APPROACH TO PERSONALITY IDENTIFICATION BY DYNAMIC BIOMETRIC PARAMETERS
Authors Yu.A. Bryukhomitsky
Section SECTION II. APPLIED INFORMATION SECURITY
Month, Year 05, 2017 @en
Index UDC 004.067
DOI
Abstract The generalized immunological approach of solving the problem of personality identification by its dynamic biometric characteristics of different modalities: voice, penscript, keyboard set, is offered. The approach is oriented to the personality identification while reproducing by the person of texts of arbitrary volume and content. The solution of this task is based on the principles of constructing and functioning of artificial immune systems, using vector representation and processing of biometric data, good conforming to the numerical character of the signals of dynamic biometry. A special feature of the approach is the presentation of signals of dynamic biometry by sequences of information units of fixed format, with their following decentralized processing based on the immunological model of negative selection. Informational units of sequences are syntactically related fragments of the text, carrying the most expressed individual characteristics of personality. The following analysis and processing of text fragments are carried out in the multidimensional metric space of attributes. Recognition of images of dynamic biometry, represented by text fragments, is realized by their comparison with recognizing elements - detectors. The comparison is carried out on the principle of negative selection. Two possible types of detectors are considered. The former are represented in the feature space by multidimensional vectors (simple detectors), the latter – by multidimensional spheres (volumetric detectors). A computational procedure for the formation of volumetric detectors at the learning stage and a procedure for comparing the signals of dynamic biometry with volumetric detectors at the identification stage are proposed. The proposed approach in the context of the immunological presentation allows us to generalize essentially different methods of identification of a person using the dynamic biometric parameters of different modalities - voice, penscript and keyboard set. The difference of approach is the possibility of text-independent analysis of texts of different modality, arbitrary volume and content. Another difference is the transition from an integrated evaluation of the results of the analysis of biometric data over a fixed period of time to a continuous evaluation of data at the rate of their receipt, with the possibility of timely making the right decision.

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Keywords Text-independent personality identification; dynamic biometry; artificial immune systems; vector data representation.
References 1. Akhmad Kh.M., Zhirkov V.F. Vvedenie v tsifrovuyu obrabotku rechevykh signalov [Introduc-tion to digital processing of speech signals]. Vladimir: Izd-vo Vladim. gos. un-ta, 2007, 192 p.
2. Matveev Yu.N. Tekhnologii biometricheskoy identifikatsii lichnosti po golosu i drugim modal'nostyam [The technology of biometric identification by voice and other modalities], Vestnik MGTU im. N.E. Baumana. Seriya Priborostroenie [Herald of the Bauman Moscow State Technical University. Series Instrument Engineering], 2012, No. 2, pp. 46-61.
3. Campbell W., Assaleh K., Broun C. Speaker recognition with polynomial classifiers, IEEE Trans. Speech Audio Process, 2002, Vol. 10, No. 4, pp. 205-212.
4. Anisimova E.S. Identifikatsiya onlayn-podpisi s pomoshch'yu okonnogo preobrazovaniya Fur'e i radial'nogo bazisa [Identification of online signatures using the windowed Fourier transform and radial basis], Komp'yuternye issledovaniya i modelirovanie [Computer research and modeling], 2014, Vol. 6, No. 3, pp. 357-364.
5. Jain A.K., Friederike D.G., Connel S.D. On-line signature verification, Pattern Recognition, 2002, Vol. 35 (12), pp. 2963-2972.
6. Plamondon R., Srihari S. On-line and Off-line Handwriting Recognition: A Comprehensive Survey, IEEE Trans. PAMI, 2000, Vol. 22 (1), pp. 63-84.
7. Ivanov A.I. Biometricheskaya identifikatsiya lichnosti po dinamike podsoznatel'nykh dvizheniy: monografiya [Biometric personal identification by dynamics of subconscious movements: monograph]. Penza: Izd-vo Penz. gos. un-ta, 2000, 188 p.
8. Bryukhomitskiy Yu.A., Kazarin M.N. Sistema autentifikatsii lichnosti po pocherku [System, person authentication by handwriting], Sbornik trudov nauchno-prakticheskoy konferentsii s mezhdunarodnym uchastiem «Informatsionnaya bezopasnost'» [Proceedings of scientific-practical conference with international participation "Information security"]. Taganrog: Izd-vo TRTU, 2002, pp. 22-29.
9. Maznichenko N.I. Gvozdenko M.V. Analiz vozmozhnostey sistem avtomaticheskoy identifikatsii klaviaturnogo pocherka [The analysis of possibilities of automatic identification systems keyboard handwriting], Vestnik Natsional'nogo tekhnicheskogo universiteta «Khar'kovskiy politekhnicheskiy institut». Seriya Informatika i modelirovanie [Bulletin of National technical University "Kharkiv Polytechnic Institute". Series Informatics and modeling], 2008, Issue No. 24, pp. 77-82.
10. Skubitskiy A.V. Analiz primenimosti metoda rekonstruktsii dinamicheskikh sistem v sistemakh biometricheskoy identifikatsii po klaviaturnomu pocherku [Analysis of the applicability of the method of reconstruction of dynamical systems in systems of biometric identification by handwriting keyboard], Infokommunikatsionnye tekhnologii [Infocommunication technologies], 2008, Vol. 6, No. 1, pp. 51-53.
11. Chalaya L.E. Model' identifikatsii pol'zovateley po klaviaturnomu pocherku [Model identifica-tion of users at the keyboard handwriting], Iskusstvennyy intellect [Artificial intelligence], 2004, No. 4, pp. 811-817.
12. Bryukhomitskiy Yu.A., Kazarin M.N. Metod biometricheskoy identifikatsii pol'zovatelya po klaviaturnomu pocherku na osnove razlozheniya Khaara i mery blizosti Khemminga [The method of biometric identification of the user at the keyboard handwriting based on the decomposition of the Haar and proximity measure of the Hamming], Izvestiya TRTU [Izvestiya TSURE], 2003, No. 4 (33), pp. 141-149.
13. Dasgupta D. Artificial Immune Systems and Their Applications. Ed., Springer-Verlag, 1999.
14. De Castro L.N., Timmis J.I. Artificial Immune Systems: A New Computational Intelligence Approach. London: Springer-Verlag, 2000, 357 p.
15. Iskusstvennye immunnye sistemy i ikh primenenie [Artificial immune systems and their appli-cations], ed. by D. Dasgupty: the translation from english A.A. Romanyukhi. Moscow: Fizmatlit, 2006, 344 p.
16. Dasgupta D., Forrest S. Tool breakage detection in milling operations using a negative-selection algorithm, Technical report CS95-5, Department of computer science, University of New Mexico, 1995.
17. Forrest S., Perelson A.S., Allen L., Cherukuri R. Self-nonself discrimination in a computer, In: Proc. of Ieee symposium on research in security, Oakland, CA, 16-18 May 1994, pp. 202-212.
18. Bryukhomitskiy Yu.A. Immunologicheskiy podkhod k organizatsii klaviaturnogo monitoringa [The immunologic approach to keyboard monitoring organization], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 2 (151), pp. 33-41.
19. Bryukhomitskiy Yu.A. Analiz rukopisnogo teksta metodami immunokomp'yutinga [Analysis of handwriting methods immunocomputing], Informatsionnoe protivodeystvie ugrozam terrorizma [Information counteraction to the terrorism threats], 2015, No. 24, pp. 36-43.
20. Bryukhomitskiy Yu.A. Immunologichekiy metod identifikatsii lichnosti po rukopisi [Immunotechnique metod of penscript analisis], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2015, No. 5 (166), pp. 174-183.
21. Bryukhomitskiy Yu.A. Immunologichekiy metod verifikatsii rukopisi s ispol'zovaniem vektornogo predstavleniya dannykh [The immunological method of penscript verification using vector representation of data], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. En-gineering Sciences], 2016, No. 9 (182), pp. 50-57.
22. Bryukhomitskiy Yu.A. Klaviaturnyy monitoring na osnove immunologicheskogo klonirovaniya [Keyboard monitoring based on immunological cloning], Bezopasnost' informatsionnykh tekhnologiy [Safety of information technology], 2016, No. 4 (40), pp. 5-11.
23. Bryukhomitskiy Yu.A. Klaviaturnaya identifikatsiya lichnosti [Keyboard identification]. Lambert Academic Publishing, Saarbrűcken, Germany, 2012, 140 p. ISBN 978-3-8484-1119-1.
24. Bryukhomitskiy Yu.A. Tsepochnyy metod klaviaturnogo monitoringa [Chain method of keyboard monitoring], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2009, No. 11 (100), pp. 135-145.
25. Ji Z., Dasgupta D. Real-valued negative selection algorithm with variable-sized detectors // Genetic and Evolutionary Computation (GECCO 2004): Proceedings. Berlin–Heidelberg: Springer-Verlag, 2004. Ser. LNCS 3102, Part I, pp. 287-298.
26. Ji Z., Dasgupta D. Revisiting negative selection algorithm, Evolutionary Computation, 2007, Vol. 15, No. 2 (Summer), pp. 223-251.
27. Ji Z., Dasgupta D. V-Detector: An Efficient Negative Selection Algorithm with «Probably Adequate» Detector Coverage, Information Sciences, 2009, Vol. 179, pp. 1390-1406.

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