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between them. Three different classifiers were applied, and in each case the key hold times were more effective features for discrimination than the interkey times. Best performance was achieved by an inductive learning classifier using both interkey and hold times. A high rate of typographical errors during login entry is reported. In practice, these are usually corrected errors-that is, they are strings which include backspaces to correct earlier errors-but their presence confounds the use of typing-style analysis as a practical means of securing access to computer systems.
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Keystroke dynamics is part of a larger class of biometrics known as behavioral biometrics, a field in which observed patterns are statistical in nature. Because of this inherent uncertainty, a commonly held belief is that behavioral biometrics are not as reliable as biometrics used for authentication
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The behavioral biometric of keystroke dynamics uses the manner and rhythm in which an individual types characters on a keyboard or keypad. The user's keystroke rhythms are measured to develop a unique biometric template of the user's typing pattern for future authentication. Keystrokes are separated
306:
The behavioral biometric of keystroke dynamics uses the manner and rhythm in which an individual types characters on a keyboard or keypad. The user's keystroke rhythms are measured to develop a unique biometric template of the user's typing pattern for future authentication. Keystrokes are separated
363:
In addition, sequences of letters may have characteristic properties for a user. In
English, the use of "the" is very common, and those three letters may be known as a rapid-fire sequence. Common endings, such as "ing", may be entered far faster than the same letters in reverse order ("gni") to the
1422:
The keystroke dynamics of a computer user's login string provide a characteristic pattern that can be used for identity verification. Timing vectors for several hundred login attempts were collected for ten "valid" users and ten "forgers", and classification analysis was applied to discriminate
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The time to seek and depress a key (seek-time) and the time the key is held down (hold-time) may be characteristic of an individual, regardless of the total speed at which they type. Most people take longer to find or get to specific letters on the keyboard than their average seek-time for all
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The benefit to keystroke dynamics (as well as other behavioral biometrics) is that FRR/FAR can be adjusted by changing the acceptance threshold at the individual level. This allows for explicitly defined individual risk mitigation that physical biometric technologies could not achieve.
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371:", "homonyms" and hold-length-errors (for a shift key held down too short or too long a time). Even without knowing what language the user is working in, these errors may be detected by looking at the rest of the text and what letters the user goes back and replaces.
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Because of these variations, any system will make false-positive and false-negative errors. Some successful commercial products have strategies to handle these issues and have proven effective in large-scale use in real-world settings and applications.
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people may be statistically faster in getting to keys they hit with their right-hand fingers than with their left-hand fingers. Index fingers may be faster than other fingers, consistent for a user, regardless of their overall speed.
327:. Using a methodology called "The Fist of the Sender," military intelligence identified that an individual had a unique way of keying in a message's "dots" and "dashes", creating a rhythm that could help distinguish ally from enemy.
339:
into static and dynamic typing, which are used to help distinguish between authorized and unauthorized users. Vibration information may be used to create a pattern for future use in both identification and authentication tasks.
307:
into static and dynamic typing, which are used to help distinguish between authorized and unauthorized users. Vibration information may be used to create a pattern for future use in both identification and authentication tasks.
1275:
Mahar, D.; Napier, R.; Wagner, M.; Laverty, W.; Henderson, R. D.; Hiron, M. (1995). "Optimizing digraph-latency based biometric typist verification systems: inter and intra typist differences in digraph latency distributions".
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Keystroke dynamic information could be used to verify or determine the identity of the person producing the keystrokes. The techniques used to do this vary widely in sophistication and range from statistical techniques to
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One of the major problems that keystroke dynamics runs into is that a user's typing varies substantially during a day and between different days and may be affected by any number of external factors.
364:
degree that varies consistently by user. This consistency may hold and reveal common sequences of the user's native language even when they are writing entirely in a different language.
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Common "errors" may also be quite characteristic of a user. There is a taxonomy of errors, such as the user's most common "substitutions", "reversals", "drop-outs", "double-strikes", "
1011:
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Keyboard dynamics received attention as a potential alternative to short PIN numbers, which were widely used for authentication early in the expansion of networked computing.
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Braund, Taylor A.; O’Dea, Bridianne; Bal, Debopriyo; Maston, Kate; Larsen, Mark E.; Werner-Seidler, Aliza; Tillman, Gabriel; Christensen, Helen (2023-05-15).
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Jenkins, Jeffrey; Nguyen, Quang; Reynolds, Joseph; Horner, William; Szu, Harold (2011-05-13). "The physiology of keystroke dynamics". In Szu, Harold (ed.).
514:
P. Nordström, J. Johansson. Security system and method for detecting intrusion in a computerized system. Patent No. 2 069 993, European Patent Office, 2009.
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measurements. As such, the traditional benchmarks of False
Acceptance Rate (FAR) and False Rejection Rates (FRR) no longer have linear relationships.
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https://web.archive.org/web/20110707084309/http://www.admitonesecurity.com/admitone_library/AOS_Compliance_Functional_Assessment_by_Coalfire.pdf
536:
Because human beings generate keystroke timings, they are not well correlated with external processes. They are frequently used as a source of
1517:
521:, A. Awad and I. Traore, "System and method for determining a computer user profile from a motion-based input device", issued 2012
1146:"Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study"
440:, Raul-Laviniu Popa, and Kyle Hunt, "Systems and Methods for Using Typing Characteristics for Authentication", issued 2024
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Bender, S and
Postley, H. (Inventors) (2007). Key sequence rhythm recognition system and method. (USA 7206938), Notes:
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October 2006). Notes: Vendor specializing in keystroke authentication solutions for government and commercial agencies.
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244:
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102:
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505:, J.R. Young and R.W. Hammon, "Method and apparatus for verifying an individual's identity", issued 1989
466:, John C. Checco, "Method for providing computer-based authentication utilizing biometrics", issued 2009
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operators began to develop unique "signatures" that could be identified simply by their tapping rhythm. As late as
1450:
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Mental health symptoms such as depression and anxiety have also been correlated with keystroke timing features.
286:. Use of patterns in key operation to identify operators predates modern computing, and has been proposed as an
295:
193:
585:. Proceedings of the IEEE International Conference on Intelligence and Security Informatics. pp. 155–160.
1600:
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1438:
479:, S. Blender and H. Postley, "Key sequence rhythm recognition system and method", issued 2007
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Bergadano, F.; Gunetti, D.; Picardi, C. (2002). "User authentication through
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letters. Which letters require more time vary dramatically and consistently for different people.
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204:
44:
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Karnan, M.Akila (2011). "Biometric personal authentication using keystroke dynamics: A review".
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Use of keylogging software may be in direct and explicit violation of local laws, such as the
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Independent
Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
973:"Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets"
918:
893:
873:
864:
Monrose, Fabian, and Aviel D. Rubin. "Keystroke dynamics as a biometric for authentication".
668:"Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets"
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610:
Monrose, Fabian, and Aviel D. Rubin. "Keystroke dynamics as a biometric for authentication".
200:
8:
1701:
1665:
1546:
1267:
Joyce, R., & Gupta, G. (1990). Identity authorization based on keystroke latencies.
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May 2006). Notes: Vendor specializing in keystroke authentication for large enterprises.
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Proceedings of the 4th ACM conference on
Computer and communications security - CCS '97
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Garcia, J. (Inventor). (1986). Personal identification apparatus. (USA 4621334). Notes:
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IEEE Transactions on
Systems, Man, and Cybernetics - Part A: Systems and Humans
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Monrose, Fabian; Rubin, Aviel (1997). "Authentication via keystroke dynamics".
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287:
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Lopatka, M. & Peetz, M.H. (2009). Vibration
Sensitive Keystroke Analysis.
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778:. Advances in Intelligent and Soft Computing. Vol. 84. pp. 113–120.
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Proceedings of the 6th ACM Conference on
Computer and Communications Security
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Proceedings of the 4th ACM conference on Computer and Communications Security
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Proceedings of the 4th ACM conference on Computer and Communications Security
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772:"A Modified Algorithm for User Identification by His Typing on the Keyboard"
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Proceedings of the 18th Annual Belgian-Dutch Conference on Machine Learning
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732:"Continuous authentication by analysis of keyboard typing characteristics"
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492:, J. Garcia, "Personal identification apparatus", issued 1986
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information generated by key-press-related events that occur when a user
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1404:(2). Institute of Electrical and Electronics Engineers (IEEE): 236–241.
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Continuous authentication by analysis of keyboard typing characteristics
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1392:
Robinson, J.A.; Liang, V.W.; Chambers, J.A.M.; MacKenzie, C.L. (1998).
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AdmitOne Security - formerly BioPassword. (vendor web-site home . URL
1105:"Continuous authentication by free-text keystroke based on CNN and RNN"
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211:. Statements consisting only of original research should be removed.
1103:
Lu, Xiaofeng; Zhang, Shengfei; Hui, Pan; Lio, Pietro (2020-09-01).
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316:
1394:"Computer user verification using login string keystroke dynamics"
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Checco, J. (2003). Keystroke Dynamics & Corporate Security.
1518:"Websites can track us by the way we type—here's how to stop it"
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1048:"Authentication of Smartphone Users Using Behavioral Biometrics"
809:"Authentication of Smartphone Users Using Behavioral Biometrics"
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Coalfire Systems Compliance Validation Assessment (2007)
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1384:(1999). Password hardening based on keystroke dynamics.
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1005:"User authentication through typing biometrics features"
700:"User authentication through typing biometrics features"
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based on physically observable characteristics such as
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Collection and potential use of keystroke dynamics data
1339:"Keystroke dynamics as a biometric for authentication"
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1388:, 73-82. Notes: Kent Ridge Digital Labs, Singapore
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ACM Transactions on Information and System Security
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Identity theft, computers and behavioral biometrics
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776:Image Processing and Communications Challenges 2
1278:International Journal of Human-Computer Studies
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396:measurement in replacement of the traditional
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736:European Convention on Security and Detection
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1046:Alzubaidi, Abdulaziz; Kalita, Jugal (2016).
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807:Alzubaidi, Abdulaziz; Kalita, Jugal (2016).
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595:: CS1 maint: multiple names: authors list (
323:, the military transmitted messages through
1052:IEEE Communications Surveys & Tutorials
813:IEEE Communications Surveys & Tutorials
53:Learn how and when to remove these messages
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1337:Monrose, Fabian; Rubin, Aviel D. (2000).
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245:Learn how and when to remove this message
227:Learn how and when to remove this message
165:Learn how and when to remove this message
103:Learn how and when to remove this message
729:
128:This article includes a list of general
1437:Vertical Company LTD. (vendor web-site
946:"Authentication via keystroke dynamics"
770:Panasiuk, Piotr; Saeed, Khalid (2010).
641:"Authentication via keystroke dynamics"
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423:, under which such use may constitute
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576:, Lior Rokach, Yuval Elovici (2009).
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375:Authentication versus identification
315:During the late nineteenth century,
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1237:iMagic Software. (vendor web-site
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1346:Future Generation Computer Systems
866:Future Generation Computer Systems
612:Future Generation Computer Systems
134:it lacks sufficient corresponding
75:tone or style may not reflect the
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1631:Handwritten biometric recognition
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538:hardware-generated random numbers
34:This article has multiple issues.
257:All information is from a study
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85:guide to writing better articles
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639:Monrose, F.; Rubin, A. (1997).
392:. Behavioral biometrics use a
42:or discuss these issues on the
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1601:Biometric voter registration
1380:Monrose, F. R. M. K., &
1352:(4). Elsevier BV: 351–359X.
784:10.1007/978-3-642-16295-4_13
345:artificial intelligence (AI)
7:
1516:Vaas, Lisa (30 July 2015).
1309:. New York, New York, USA:
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415:Legal and regulatory issues
274:refer to the collection of
207:the claims made and adding
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16:Biometrics from keystrokes
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1269:Communications of the ACM
971:Deng, Y.; Yu, Y. (2013).
923:. MIT Press. p. 23.
666:Deng, Y.; Yu, Y. (2013).
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1109:Computers & Security
917:Haring, Kristen (2007).
730:Shepherd, S.J. (1995).
149:more precise citations.
1596:Biometric tokenization
1290:10.1006/ijhc.1995.1061
977:ISRN Signal Processing
672:ISRN Signal Processing
540:for computer systems.
438:US patent 11914690
1661:Signature recognition
1606:Eye vein verification
1591:Biometrics in schools
1319:10.1145/266420.266434
1223:10.1145/581271.581272
958:10.1145/266420.266434
653:10.1145/266420.266434
519:US patent 8230232
503:US patent 4805222
490:US patent 4621334
477:US patent 7206938
464:US patent 7509686
451:US patent 9430626
1201:WSTA Ticker Magazine
894:"Keystroke Dynamics"
738:. pp. 111–114.
369:adjacent letter hits
265:keystroke biometrics
1666:Speaker recognition
1581:Biometric passports
1495:. pp. 80581N.
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990:10.1155/2013/565183
744:10.1049/cp:19950480
685:10.1155/2013/565183
347:approaches such as
269:typing dynamics, or
1651:Private biometrics
1641:Keystroke dynamics
1491:. Vol. 8058.
1453:2009-03-24 at the
1313:. pp. 48–56.
1150:JMIR Mental Health
952:. pp. 48–56.
647:. pp. 48–56.
261:Keystroke dynamics
192:possibly contains
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1501:10.1117/12.887419
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793:978-3-642-16294-7
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36:Please help
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1702:Biometrics
1691:Categories
1570:Biometrics
1531:2018-02-01
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1333:much cited
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559:References
532:Other uses
394:confidence
325:Morse Code
201:improve it
130:references
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1366:0167-739X
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398:pass/fail
298:numbers.
292:passwords
276:biometric
205:verifying
45:talk page
1451:Archived
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547:See also
317:telegram
284:keyboard
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311:History
302:Science
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