The DOI matches its Crossref record.
Computer-access security systems using keystroke dynamics
Abstract
An approach to securing access to computer systems is described. By performing real-time measurements of the time durations between the keystrokes when a password is entered and using pattern-recognition algorithms, three online recognition systems were devised and tested. Two types of passwords were considered: phrases and individual names. A fixed phrase was used in the identification system. Individual names were used as a password in the verification system and in the overall recognition system. All three systems were tested and evaluated. The identification system used 10 volunteers and gave an indecision error of 1.2%. The verification system used 26 volunteers and gave an error of 8.1% in rejecting valid users and an error of 2.8% in accepting invalid users. The overall recognition system used 32 volunteers and gave an error of 3.1% in rejecting valid users and an error of 0.5% in accepting invalid users.>
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Keystroke dynamics as a biometric for authentication
Monrose, F., & Rubin, A. D. (2000) · Future Generation Computer Systems · doi:10.1016/s0167-739x(99)00059-x · Cited by 745
Comparing anomaly-detection algorithms for keystroke dynamics
Killourhy, K. S., & Maxion, R. A. (2009) · 2009 IEEE/IFIP International Conference on Dependable Systems & Networks · doi:10.1109/dsn.2009.5270346 · Cited by 566
A review of biometric technology along with trends and prospects
Unar, J. A., Seng, W. C., & Abbasi, A. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.01.016 · Cited by 404
Biometric Authentication and Identification Using Keystroke Dynamics: A Survey
Banerjee, S. P., & Woodard, D. (2012) · Journal of Pattern Recognition Research · doi:10.13176/11.427 · Cited by 369
A Survey of Keystroke Dynamics Biometrics
Teh, P. S., Teoh, A. B. J., & Yue, S. (2013) · The Scientific World Journal · doi:10.1155/2013/408280 · Cited by 329
Metadata
- Authors
- Bleha, S., Slivinsky, C., & Hussien, B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1990
- Volume
- 12
- Issue
- 12
- Pages
- 1217-1222
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2158877422
- Cited by
- 312
- References
- 0
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computer-access security systems using keystroke dynamics. https://app.cochat.ai/papers/10.1109/34.62613
- The paper
- Bleha, S., Slivinsky, C., & Hussien, B. (1990). Computer-access security systems using keystroke dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.62613
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
