Features of keystroke dynamics and their forensic significance: literature review
https://doi.org/10.30729/2541-8823-2024-9-3-145-164
Abstract
Keystroke dynamics is a phenomenon deeply studied in computer science. Its research began in the second half of the 1970s and immediately yielded positive results: keystroke dynamics was found to be unique and relatively stable — ergo, significant for identification. The first studies focused on three features of keystroke dynamics: typing speed, key press time, and the time between two consecutive key presses. Today, research on this topic has not stopped, but, on the contrary, has intensified due to the general increase in interest in biometric characteristics of personality. Scientists identify new features of keyboard handwriting that form the basis of identification: the strength of keystrokes, the use of service keys, the nature of overlapping, the nature and frequency of typing irregularities, arrhythmic input, spatial orientation of keystrokes and others. At the same time, printed texts are rapidly replacing manuscripts every year, which leads to the emergence of a new forensic task: to identify the executor of a printed text with criminal content (extremist material on the Internet; a post in a “death group” on a social network; information on the location of a narcotic substance, etc.). In this case, the legal and forensic study of keystroke dynamics becomes useful, because it is through it that it is possible to establish who exactly typed the text, avoiding the prosecution of an innocent person whose account was used by the attackers. However, for this to be possible, lawyers must adapt the results achieved by colleagues from computer science to the normative procedure of criminal proceedings, which requires, first of all, a comprehensive understanding of the current level of research in this area and the features of keystroke dynamics, which will then form a forensically significant set necessary for accurate identification. Scientists already pay their attention to this phenomenon, but the features they suggest are not exhaustive and even the most widespread, in this regard, there is an obvious need to systematize the available developments and identify those features of keystroke dynamics, which are the highest priority for their use in order to solve and investigate crimes. It is to present this information that the present work has been prepared.
Keywords
keyboard handwriting,
biometric characteristic,
biometrics,
forensic identification,
keyboard handwriting features,
performer identification,
computer crimes
Supporting agencies: The research was supported by the grant of the Russian Science Foundation № 23-78-10011; https://rscf.ru/project/23-78-10011/.
About the Authors
A. D. Tsvetkova
V. F. Yakovlev Ural State Law University
Russian Federation
Russian Federation
Anna Tsvetkova — student, intern researcher of the scientific Laboratory “Digital Technologies in Criminalistics” of the Department of Scientific Research
21 Komsomolskaya St., Yekaterinburg, 620137
D. V. Bakhteev
V. F. Yakovlev Ural State Law University
Russian Federation
Russian Federation
Dmitriy Bakhteev— Professor of the Department of Criminalistics, Principal Researcher of the Department of Scientific Research, Doctor of Legal Sciences, Associate Professor
21 Komsomolskaya St., Yekaterinburg, 620137
References
1. Alekseev A. A., Voevodin V. A., Prokhorova V. V. Klaviaturnyy pocherk kak sredstvo autentifikatsii subekta dostupa k informatsionnym resursam [Keyboard handwriting as a means of authentication of the subject of access to information resources] // Materialy nauchno-tekhnicheskoy konferentsii “Mikroelektronika i informatika — 2022”: Sbornik statey konferentsii, Moskva, 21–22 aprelya 2022 goda. Moskva: Natsionalnyy issledovatelskiy universitet “Moskovskiy institut elektronnoy tekhniki” [Proceedings of the Scientific and Technical Conference “Microelectronics and Informatics — 2022”: Collection of conference papers, Moscow, 21–22 April 2022. Moscow: National Research University “Moscow Institute of Electronic Technology”], 2022. Pp. 3–7. (In Russian)
2. Amer M., Bari M. A., Khare A. Fingerprint Image Identification for Crime Detection // International Journal for Advanced Research in Science & Technology. 2022. Vol. 12. Issue 10. Pp. 144–126. (In English)
3. Batskikh A. V., Drovnikova I. G., Zarubin V. S. Bazovye aspekty modifikatsii podsistem upravleniya dostupom k informatsii v avtomatizirovannykh sistemakh organov vnutrennikh del na osnove issledovaniya klaviaturnogo pocherka polzovateley [Basic aspects of modification of subsystems of information access control in automated systems of internal affairs bodies on the basis of research of keyboard handwriting of users] // Vestnik Voronezhskogo instituta MVD Rossii [Bulletin of the Voronezh Institute of the Ministry of Internal Affairs of Russia]. 2022. No. 4. Pp. 21–32. (In Russian)
4. Boriskin S.M. Razrabotka protsedur, realizuyushchikh autentifikatsiyu i registratsiyu parametrov kompyuternogo pocherka pri kompleksnom podkhode k autentifikatsii [Development of procedures realising authentication and registration of computer handwriting parameters at the complex approach to authentication] // Estestvennye i tekhnicheskie nauki [Natural and Technical Sciences]. 2010. No. 5 (48). Pp. 473–478. (In Russian)
5. Brown M., Rogers S. J. User identification via keystroke characteristics of typed names using neural networks // Int. J. Man-Mach. Stud. 1993. Vol. 39 (6). Pp. 999– 1014. (In English)
6. Bryan W. L., Harter N. Studies in the physiology and psychology of the telegraphic language // Psychological review. 1987. №4 (1). Pp. 27–53. (In English) Bryukhomitskiy Yu. A., Kazarin M. N. Vydelenie informativnykh biometricheskikh parametrov v sistemakh klaviaturnogo monitoring [Selection of informative biometric parameters in keyboard monitoring systems] // Informatsionnoe protivodeystvie ugrozam terrorizma [Information counteraction to threats of terrorism]. 2010. No. 14. Pp. 139–143. (In Russian)
7. Chen M. H., Leow A., Ross M. K. [et al]. Associations between smartphone keystroke dynamics and cognition in MS // Digital Health. 2022. Vol. 8. Pp. 1–12. (In English)
8. Dai X., Zhao R., Hu P., Munteanu A. KD-Net: Continuous Keystroke Dynamics Based Human Identification from RGB-D Image Sequences // Sensor. 2023. Vol. 23 (20). Pp. 1–16. (In English)
9. Ekwunife R. A., Ojiaku K., Ukeje I. O. Evaluation of CCTV And Biometrics as ICT Tools for Curbing Criminality in Nigeria: A Study of Ebonyi State Police Command Abakaliki // Akpauche: International Journal of Arts and Social Sciences. 2020. Vol. 1. No. 2. Pp. 96–105. (In English)
10. Eremenko A. V., Sulavko A. E., Mishin D. V., Fedotov A. A. Identifikatsionnyy potentsial klaviaturnogo pocherka s uchetom parametrov vibratsii i sily nazhatiya na klavishi [Identification potential of keyboard handwriting taking into account the parameters of vibration and force of pressing the keys] // Prikladnaya informatika [Applied Informatics]. 2017. Vol. 12. No. 1 (67). Pp. 79–94. (In Russian)
11. Fedorov I. Z. K voprosu ob ustanovlenii ispolnitelya elektronnogo teksta po klaviaturnomu pocherku pri raskrytii i rassledovanii prestupleniy [To the issue of establishing the executor of electronic text by keyboard handwriting in the detection and investigation of crimes] // Vestnik Barnaulskogo yuridicheskogo instituta MVD Rossii [Bulletin of Barnaul Law Institute of the Ministry of Internal Affairs of Russia]. 2019. No. 2 (37). Pp. 113–116. (In Russian)
12. Fedorov V.M., Rublev D.P. Obrabotka vibroakusticheskikh shumov, voznikayushchikh pri rabote polzovatelya s klaviaturoy [Processing of vibroacoustic noises occurring when a user works with a keyboard] // Izvestiya YuFU. Tekhnicheskie nauki [Bulletin of the Southern Federal University. Technical Sciences]. 2012. No. 12 (137). Pp. 75–81. (In Russian)
13. Forsen G., Nelson M., Staron R. Jr. Personal attributes authentication techniques. Technical Report RADC-TR-77-333. Rome: Air Development Center, 1977. 333 p. (In English)
14. Foygel E. I. Nekotorye vozmozhnosti ispolzovaniya povedencheskoy biometrii v rassledovanii prestupleniy [Some possibilities of using behavioral biometrics in crime investigation] // Razvitie rossiyskogo obshchestva: vyzovy sovremennosti: Materialy natsionalnoy nauchno-prakticheskoy konferentsii s mezhdunarodnym uchastiem, posvyashchennoy 90-letiyu Baykalskogo gosudarstvennogo universiteta, Irkutsk, 15–16 oktyabrya 2020 goda. Irkutsk: Baykalskiy gosudarstvennyy universitet [Development of Russian society: challenges of modernity: Proceedings of the national scientific-practical conference with international participation, dedicated to the 90th anniversary of Baikal State University, Irkutsk, 15–16 October 2020. Irkutsk: Baikal State University], 2021. Pp. 417–420. (In Russian)
15. Gaines R., Lisowski W., Press S., Shapiro N. Authentication by keystroke timing: some preliminary results. Rand Rep. R-2560-NSF, Rand Corporation, 1980. 51 p. (In English)
16. Golovenchik G. G. Problemy kiberbezopasnosti v usloviyakh tsifrovoy transformatsii ekonomiki i obshchestva [Problems of cyber security in the conditions of digital transformation of economy and society] // Ekonomika. Upravlenie. Innovatsii [Economics. Management. Innovations]. 2018. No. 2 (4). Pp. 23–33. (In Russian)
17. Guzik V. F., Desyaterik M. N. Biometricheskiy metod autentifikatsii polzovatelya [Biometric method of user authentication] // Izvestiya TRTU. 2000. No. 2 (16). Pp. 129–133. (In Russian)
18. Joyce R., Gupta G. Identity authorization based on keystroke latencies // Commun. ACM. 1990. Vol. 33 (2). Pp. 168–176. (In English)
19. Khmyz A. I. Razgranichenie identifikatsionnykh priznakov pri ustanovlenii interaktivnogo polzovatelya [Distinguishing the identification attributes when establishing an interactive user] // Tsifrovaya transformatsiya: obrazovanie, nauka, obshchestvo. Moskva: Avtonomnaya nekommercheskaya organizatsiya Tsentralnyy nauchno-issledovatelskiy institut russkogo zhestovogo yazyka [Digital Transformation: Education, Science, Society. Moscow: Autonomous non-profit organization Central Research Institute of Russian Sign Language], 2019. Pp. 343–350. (In Russian)
20. Kolakowska A. Generating training data for SART-2 keystroke analysis module // Proceedings of the 2nd International Conference on Information Technology (ICIT’10). 2010. Pp. 57–60. (In English)
21. Latt Ch. V. Vliyanie izmerennykh kharakteristik “pocherka” polzovatelya vychislitelnoy seti na veroyatnost ego identifikatsii logicheskoy neyronnoy setyu po etalonu [Influence of the measured characteristics of the “handwriting” of a computer network user on the probability of his identification by a logical neural network according to the standard] // Estestvennye i tekhnicheskie nauki [Natural and Technical Sciences]. 2011. No. 2 (52). Pp. 420–422. (In Russian)
22. Le T. H., Le B. Keystroke dynamics extraction by independent component analysis and bio-matrix for user authentication // Zhang B. T., Orgun M. A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science. Vol. 6230. Berlin, Heidelberg: Springer, 2010. Pp. 477–486. (In English)
23. Liu W.-M., Yeh C.-L., Chen P.-W. [et al]. Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson’s Disease // Diagnostics. 2023. Vol. 13 (19). No. 3061. Pp. 1–11. (In English)
24. Lozhnikov P. S., Sulavko A. E., Buraya E. V., Eremenko A. V. Sposoby generatsii klyuchevykh posledovatelnostey na osnove klaviaturnogo pocherka [Methods of key sequence generation based on keyboard handwriting] // Dinamika sistem, mekhanizmov i mashin [Dynamics of systems, mechanisms and machines]. 2016. No. 4. Pp. 265–270. (In Russian)
25. Lv H.-R., Wang W.-Y. Biologic verification based on pressure sensor keyboards and classifier fusion techniques // IEEE Transactions on Consumer Electronics. 2006. Vol. 52 (3). Pp. 1057–1063. (In English)
26. Miller G. A. The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information // The Psychological Review. 1956. Vol. 63. Pp. 81–97. (In English)
27. Mondol S. K., Tang W., Hasan S. A. A Case Study of IoT Based Biometric Cyber Security Systems Focused on the Banking Sector // International Conference on Expert Clouds and Applications, India, March 2023. 2023. Pp. 1–13. (In English)
28. Monrose F., Rubin A. Authentication via Keystroke Dynamics // Proceedings of the Fourth ACM Conference on Computer and Communication Security. 1997. Pp. 48–56. (In English)
29. Nguyen T. T., Le T. H., Le B. H. Keystroke dynamics extraction by independent component analysis and bio-matrix for user authentication // Proceedings of the 11th Pacific Rim International Conference on Trends in Artificial Intelligence, Daegu, Republic of Korea. 2010. Pp. 477–486. (In English)
30. Nikulicheva E. O. Analiz klaviaturnogo pocherka kak metod identifikatsii lichnosti [Analysis of keyboard handwriting as a method of personal identification] // Aktualnye voprosy sudebnoy psikhologicheskoy ekspertizy i kompleksnoy ekspertizy s uchastiem psikhologa. Perspektivy nauchnogo i prikladnogo issledovaniya pocherka: Sbornik materialov III mezhdunarodnoy nauchno-prakticheskoy konferentsii, Kaluga, 16–19 maya 2019 goda [Actual issues of forensic psychological examination and complex examination with the participation of a psychologist. Perspectives of scientific and applied research of handwriting: Collection of materials of the III International Scientific and Practical Conference, Kaluga, 16–19 May 2019] / Pod redaktsiey V. F. Engalycheva, E. V. Leonovoy. Kaluga: FBGOU VPO “Kaluzhskiy gosudarstvennyy universitet im. K. E. Tsiolkovskogo”, 2019. Pp. 56–60. (In Russian)
31. Nonaka H., Kurihara M. Sensing Pressure for Authentication System Using Keystroke Dynamics // International Journal of Computer, Control, Quantum and Information Engineering. 2007. Vol. 1. № 1. Pp. 152–155. (In English)
32. Panfilova I. E., Karpova N. E. Issledovanie vliyaniya sostoyaniya polzovatelya na kachestvo autentifikatsii po klaviaturnomu pocherku [Investigation of the influence of the user state on the quality of authentication by keyboard handwriting] // Dinamika sistem, mekhanizmov i mashin [Dynamics of systems, mechanisms and machines]. 2021. Vol. 9. No. 4. Pp. 68–74. (In Russian)
33. Pisani P. H., Lorena A. C. A systematic review on keystroke dynamics // Journal of the Brazilian Computer Society. 2013. Vol. 19 (4). Рp. 573–585. (In English)
34. Presnukhin R. S. Postroenie modeli protsedury autentifikatsii dlya doverennogo nositelya informatsii na baze flash-nakopitelya [Building a model of authentication procedure for a trusted flash-based storage medium] // Vestnik Nauki i Tvorchestva [Bulletin of Science and Creativity]. 2016. No. 5 (5). Pp. 378–383. (In Russian)
35. Princy A. T., Lakshmi P. A., Suvanam S. B. A Review of Behaviometric Techniques for User Authentication // IOSR Journal of Computer Engineering (IOSR-JCE). 2016. Pp. 11–14. (In English)
36. Rastorguev S. P. Programmnye metody zashchity informatsii v kompyuterakh i setyakh [Programme methods of information protection in computers and networks]. M.: Izdatelstvo Agentstva “Yakhtsmen”, 1993. 188 p. (In Russian)
37. Sapiev A. Z. Kompyuternyy pocherk kak sposob identifikatsii polzovateley v seti [Computer handwriting as a way to identify users in the network] // Vestnik Vologodskogo gosudarstvennogo universiteta. Seriya: Tekhnicheskie nauki [Bulletin of Vologda State University. Series: Technical Sciences]. 2021. No. 4 (14). Pp. 17–19. (In Russian)
38. Sapiev A. Z. Priznaki identifikatsii polzovateley informatsionnykh sistem po kompyuternomu pocherku [Signs of identification of information systems users by computer handwriting] // Informatsionnye tekhnologii v modelirovanii i upravlenii: podkhody, metody, resheniya: IV Vserossiyskaya nauchnaya konferentsiya s mezhdunarodnym uchastiem: Sbornik materialov, Tolyatti, 20–22 aprelya 2021 goda [Information technologies in modelling and management: approaches, methods, solutions: IV All-Russian scientific conference with international participation: Proceedings, Togliatti, 20–22 April]. Tolyatti: Tolyattinskiy gosudarstvennyy universitet, 2021. Pp. 157–161. (In Russian)
39. Shangina I. Yu. Tekhnologii biometricheskoy identifikatsii: mirovaya i Rossiyskaya praktiki [Biometric identification technologies: world and Russian practice] // Innovatsii. Nauka. Obrazovanie [Innovations. Science. Education]. 2020. No. 18. Pp. 151–156. (In Russian)
40. Siahaan C. R. P., Chowanda A. Spoofing keystroke dynamics authentication through synthetic typing pattern extracted from screen-recorded video // Journal of Big Data. 2022. Vol. 9 (111). Pp. 1–29. (In English)
41. Spillane R. Keyboard Apparatus for Personal Identification // IBM Technical Disclosure Bulletin. 1975. Vol. 17. № 3346. (In English)
42. Stylios I., Kokolakis S., Thanou O., Chatzis S. Behavioral Biometrics & Continuous User Authentication on Mobile Devices: A Survey // Information Fusion. 2021. Vol. 66. Pp. 76–99. (In English)
43. Sulavko A. E., Shalina E. V. Biometricheskaya autentifikatsiya polzovateley informatsionnykh sistem po klaviaturnomu pocherku na osnove immunnykh setevykh algoritmov [Biometric authentication of information system users by keyboard handwriting based on immune network algorithms] // Prikladnaya informatika [Applied Informatics]. 2019. Vol. 14. No. 3 (81). Pp. 39–54. (In Russian)
44. Syed I. S. Z. Soft Biometrics for Keystroke Dynamics. Computer Vision and Pattern Recognition. Universit´e de Caen Basse-Normandie, 2014. 134 p. (In English)
45. Turutina E. E. Analiz metodov elektronnoy i biometricheskoy autentifikatsii v sistemakh kontrolya dostupom [Analysis of electronic and biometric authentication methods in access control systems] // Vestnik NTsBZhD [Bulletin of the Life Safety Research Center]. 2021. No. 2 (48). Pp. 168–175. (In Russian)
46. Vacca J. R. Biometric technologies and verification systems. Oxford: USA Linacre House, 2007. 625 p. (In English)
47. Varlamova S. A., Vavilina E. A. Identifikatsiya polzovatelya na osnove klaviaturnogo pocherka [User identification based on keyboard handwriting] // Innovatsionnoe priborostroenie [Innovation Instrument Engineering]. 2023. Vol. 2. No. 3. Pp. 67–71. (In Russian)
48. Varnashina P.D., Bushueva M.E. Issledovanie emotsionalnogo sostoyaniya cheloveka po klaviaturnomu pocherku [Study of the emotional state of a person by keyboard handwriting] // Informatsionnye sistemy i tekhnologii — 2019: Sbornik materialov XXV Mezhdunarodnoy nauchno-tekhnicheskoy konferentsii, Nizhniy Novgorod, 19 aprelya 2019 goda. Nizhniy Novgorod: Nizhegorodskiy gosudarstvennyy tekhnicheskiy universitet im. R. E. Alekseeva [Information Systems and Technologies — 2019: Proceedings of the XXV International Scientific and Technical Conference, Nizhny Novgorod, 19 April 2019. Nizhny Novgorod: R. E. Alekseev Nizhny Novgorod State Technical University], 2019. Pp. 591–595. (In Russian)
49. Vorona V. A., Kostenko V. O. Biometricheskie tekhnologii identifikatsii v sistemakh kontrolya i upravleniya dostupom [Biometric identification technologies in access control and management systems] // Computational Nanotechnology. 2016. No. 3. Pp. 224–241. (In Russian)
50. Zhu H., Li C., Yu B. [et al]. Research on the Key Application of Computer Biometric Technology in Power Self-Service Terminal // Advances in Artificial Intelligence, Big Data and Algorithms / G. Grigoras and P. Lorenz (Eds.). 2023. Pp. 943–948. (In English)
Review
For citations:
Tsvetkova A.D., Bakhteev D.V. Features of keystroke dynamics and their forensic significance: literature review. Kazan University Law Review. 2024;9(3):145-164. https://doi.org/10.30729/2541-8823-2024-9-3-145-164
Views:
75
Email this article 