{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:41:11Z","timestamp":1773157271552,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometrics can support verification without bothering the user with a requirement of an additional interaction. Our research aimed to check whether using information about how partial passwords are typed is possible to strengthen user authentication security. The partial password is a query of a subset of characters from a full password. The use of partial passwords makes it difficult for attackers who can observe password entry to acquire sensitive information. In this paper, we use a Siamese neural network and n-shot classification using past recent logins to verify user identity based on keystroke dynamics obtained from the static text. The experimental results on real data demonstrate that keystroke dynamics authentication can be successfully used for partial password typing patterns. Our method can support the basic authentication process and increase users\u2019 confidence.<\/jats:p>","DOI":"10.3390\/s23156685","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T02:14:48Z","timestamp":1690424088000},"page":"6685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8480-8434","authenticated-orcid":false,"given":"Kamila","family":"Lis","sequence":"first","affiliation":[{"name":"Research and Academic Computer Network, Kolska 12, 01-045 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4782-3816","authenticated-orcid":false,"given":"Ewa","family":"Niewiadomska-Szynkiewicz","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15\/19, 00-665 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4462-3927","authenticated-orcid":false,"given":"Katarzyna","family":"Dziewulska","sequence":"additional","affiliation":[{"name":"Research and Academic Computer Network, Kolska 12, 01-045 Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"ref_1","unstructured":"(2023, April 21). NIST Special Publication 800-63B-Digital Identity Guidelines, Available online: https:\/\/pages.nist.gov\/800-63-3\/sp800-63b.html#memsecretver."},{"key":"ref_2","first-page":"213","article-title":"Keystroke dynamics based authentication","volume":"479","author":"Obaidat","year":"1996","journal-title":"Biom. Pers. Identif. Networked Soc."},{"key":"ref_3","unstructured":"Maxion, R.A., and Commuri, V. (2020). This Is Your Behavioral Keystroke Biometric on Rubbish Data, Carnegie Mellon University."},{"key":"ref_4","unstructured":"Solano, J., Rivera, E., Castelblanco, A., Tengana, L., Lopez, C., and Ochoa, M. (2021, January 3\u20137). A Siamese Neural Network for Behavioral Biometrics Authentication. Proceedings of the ICLR 2021 Conference, Virtual Event, Austria."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s40537-022-00662-8","article-title":"Spoofing keystroke dynamics authentication through synthetic typing pattern extracted from screen-recorded video","volume":"9","author":"Siahaan","year":"2022","journal-title":"Big Data"},{"key":"ref_6","unstructured":"Sadeghi, A.R. (2013, January 1\u20135). \u201cGive Me Letters 2, 3 and 6!\u201d: Partial Password Implementations and Attacks. Proceedings of the Financial Cryptography and Data Security, Okinawa, Japan. Lecture Notes in Computer Science."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"116","DOI":"10.13176\/11.427","article-title":"Biometric Authentication and Identification Using Keystroke Dynamics: A Survey","volume":"7","author":"Banerjee","year":"2012","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1037\/h0073806","article-title":"Studies in the physiology and psychology of the telegraphic language","volume":"4","author":"Lowe","year":"1897","journal-title":"Psychol. Rev."},{"key":"ref_9","unstructured":"Coppenrath, L.F. (2022, March 07). Biopassword Technology Overview. Available online: http:\/\/www.lfca.net\/Reference%20Documents\/Biometric%20Technology%20Overview.pdf."},{"key":"ref_10","unstructured":"Gaines, R.S., Lisowski, W., Press, S.J., and Shapiro, N. (1980). Authentication by Keystroke Timing: Some Preliminary Results, RAND Corporation."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0020-7373(85)80036-5","article-title":"Identity verification through keyboard characteristics","volume":"23","author":"Umphress","year":"1985","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1145\/75577.75582","article-title":"Identity Authentication Based on Keystroke Latencies","volume":"33","author":"Joyce","year":"1990","journal-title":"Commun. ACM"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gunetti, D., Picardi, C., and Ruffo, G. (2005, January 21\u201323). Dealing with Different Languages and Old Profiles in Keystroke Analysis of Free Text. Proceedings of the 9th Congress of the Italian Association for Artificial Intelligence, Milan, Italy.","DOI":"10.1007\/11558590_36"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Killourhy, K.S., and Maxion, R.A. (July, January 29). Comparing anomaly-detection algorithms for keystroke dynamics. Proceedings of the 2009 IEEE\/IFIP International Conference on Dependable Systems Networks, Lisbon, Portugal.","DOI":"10.1109\/DSN.2009.5270346"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"408280","DOI":"10.1155\/2013\/408280","article-title":"A Survey of Keystroke Dynamics Biometrics","volume":"2013","author":"Teh","year":"2013","journal-title":"Sci. World J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Giot, R., and Rocha, A. (2019, January 9\u201312). Siamese Networks for Static Keystroke Dynamics Authentication. Proceedings of the 2019 IEEE International Workshop on Information Forensics and Security (WIFS), Delft, The Netherlands.","DOI":"10.1109\/WIFS47025.2019.9035100"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/TBIOM.2020.3003988","article-title":"Fast Free-Text Authentication via Instance-Based Keystroke Dynamics","volume":"2","author":"Ayotte","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TBIOM.2021.3112540","article-title":"TypeNet: Deep Learning Keystroke Biometrics","volume":"4","author":"Acien","year":"2021","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wahab, A., and Hou, D. (2023, January 22\u201324). When Simple Statistical Algorithms Outperform Deep Learning: A Case of Keystroke Dynamics. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM, Lisbon, Portugal.","DOI":"10.5220\/0011684100003411"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wahab, A., Hou, D., and Schuckers, S. (2023, January 24\u201326). A User Study of Keystroke Dynamics as Second Factor in Web MFA. Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy, Charlotte, NC, USA.","DOI":"10.1145\/3577923.3583642"},{"key":"ref_21","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 29). Signature Verification Using a \u201cSiamese\u201d Time Delay Neural Network. Proceedings of the 6th International Conference on Neural Information Processing Systems, San Francisco, CA, USA."},{"key":"ref_22","unstructured":"Koch, G.R. (2015, January 6\u201311). Siamese Neural Networks for One-Shot Image Recognition. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_23","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_24","unstructured":"Fomin, V., Anmol, J., Desroziers, S., Kriss, J., and Tejani, A. (2023, May 22). High-Level Library to Help with Training Neural Networks in PyTorch. Available online: https:\/\/github.com\/pytorch\/ignite."},{"key":"ref_25","unstructured":"Li, Y., Zhang, B., Cao, Y., Zhao, S., Gao, Y., and Liu, J. (2011, January 11\u201313). Study on the BeiHang Keystroke Dynamics Database. Proceedings of the 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Giot, R., El-Abed, M., and Christophe, R. (2009, January 28\u201330). GREYC Keystroke: A Benchmark for Keystroke Dynamics Biometric Systems. Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2009), Washington, DC, USA.","DOI":"10.1109\/BTAS.2009.5339051"},{"key":"ref_27","unstructured":"(2023, May 22). Norton Password Manager. Available online: https:\/\/my.norton.com\/extspa\/passwordmanager?path=pwd-gen."},{"key":"ref_28","unstructured":"Conrad, E., Misenar, S., and Feldman, J. (2012). CISSP Study Guide, Syngress. [2nd ed.]."},{"key":"ref_29","unstructured":"(2023, February 23). Innovatrics. Equal Error Rate (EER) Definition. Available online: https:\/\/www.innovatrics.com\/glossary\/equal-error-rate-eer\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-Generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_31","first-page":"49","article-title":"On the generalized distance in statistics","volume":"2","author":"Mahalanobis","year":"1936","journal-title":"Proc. Natl. Inst. Sci. Calcutta"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6685\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:19:20Z","timestamp":1760127560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":31,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156685"],"URL":"https:\/\/doi.org\/10.3390\/s23156685","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,26]]}}}