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Research on keystroke dynamics has the good potential to offer continuous authentication that complements conventional authentication methods in combating insider threats and identity theft before more harm can be done to the genuine users. Unfortunately, the large amount of data required by free-text keystroke authentication often contain personally identifiable information, or PII, and personally sensitive information, such as a user's first name and last name, username and password for an account, bank card numbers, and social security numbers. As a result, there are privacy risks associated with keystroke data that must be mitigated before they are shared with other researchers. We conduct a systematic study to remove PII's from a recent large keystroke dataset. We find substantial amounts of PII's from the dataset, including names, usernames and passwords, social security numbers, and bank card numbers, which, if leaked, may lead to various harms to the user, including personal embarrassment, blackmails, financial loss, and identity theft. We thoroughly evaluate the effectiveness of our detection program for each kind of PII. We demonstrate that our PII detection program can achieve near perfect recall at the expense of losing some useful information (lower precision). Finally, we demonstrate that the removal of PII's from the original dataset has only negligible impact on the detection error tradeoff of the free-text authentication algorithm by Gunetti and Picardi. We hope that this experience report will be useful in informing the design of privacy removal in future keystroke dynamics based user authentication systems.
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Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism [Utilizing Keystroke Dynamics as Additional Security Measure to Protect Account Recovery Mechanism]
Account recovery is ubiquitous across web applications but circumvents the username/password-based login step. Therefore, it deserves the same level of security as the user authentication process. A common simplistic procedure for account recovery requires that a user enters the same email used during registration, to which a password recovery link or a new username could be sent. Therefore, an impostor with access to a user’s registration email and other credentials can trigger an account recovery session to take over the user’s account. To prevent such attacks, beyond validating the email and other credentials entered by the user, our proposed recovery method utilizes keystroke dynamics to further secure the account recovery mechanism. Keystroke dynamics is a type of behavioral biometrics that uses the analysis of typing rhythm for user authentication. Using a new dataset with over 500,000 keystrokes collected from 44 students and university staff when they fill out an account recovery web form of multiple fields, we have evaluated the performance of five scoring algorithms on individual fields as well as feature-level fusion and weighted-score fusion. We achieve the best EER of 5.47% when keystroke dynamics from individual fields are used, 0% for a feature-level fusion of five fields, and 0% for a weighted-score fusion of seven fields. Our work represents a new kind of keystroke dynamics that we would like to call it ‘medium fixed-text’ as it sits between the conventional (short) fixed text and (long) free text research.
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- Award ID(s):
- 1650503
- PAR ID:
- 10323029
- Date Published:
- Journal Name:
- 7th International Conference on Information Systems Security and Privacy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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As account compromises and malicious online attacks are on the rise, multi-factor authentication (MFA) has been adopted to defend against these attacks. OTP and mobile push notification are just two examples of the popularly adopted MFA factors. Although MFA improve security, they also add additional steps or hardware to the authentication process, thus increasing the authentication time and introducing friction. On the other hand, keystroke dynamics-based authentication is believed to be a promising MFA for increasing security while reducing friction. While there have been several studies on the usability of other MFA factors, the usability of keystroke dynamics has not been studied. To this end, we have built a web authentication system with the standard features of signup, login and account recovery, and integrated keystroke dynamics as an additional factor. We then conducted a user study on the system where 20 participants completed tasks related to signup, login and account recovery. We have also evaluated a new approach for completing the user enrollment process, which reduces friction by naturally employing other alternative MFA factors (OTP in our study) when keystroke dynamics is not ready for use. Our study shows that while maintaining strong security (0% FPR), adding keystroke dynamics reduces authentication friction by avoiding 66.3% of OTP at login and 85.8% of OTP at account recovery, which in turn reduces the authentication time by 63.3% and 78.9% for login and account recovery respectively. Through an exit survey, all participants have rated the integration of keystroke dynamics with OTP to be more preferable to the conventional OTP-only authentication.more » « less
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Keystroke dynamics are a powerful behavioral biometric capable of determining user identity and for continuous authentication. It is an unobtrusive method that can complement an existing security system such as a password scheme and provides continuous user authentication. Existing methods record all keystrokes and use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Current state-of-the-art algorithms report EER’s of 7.5% or higher with 1000 characters. With 1000 characters it takes a longer time to detect an imposter and significant damage could be done. In this paper, we investigate how quickly a user is authenticated or how many digraphs are required to accurately detect an imposter in an uncontrolled free-text environment. We present and evaluate the effectiveness of three distance metrics individually and fused with each other. We show that with just 100 digraphs, about the length of a single sentence, we achieve an EER of 35.3%. At 200 digraphs the EER drops to 15.3%. With more digraphs, the performance continues to steadily improve. With 1000 digraphs the EER drops to 3.6% which is an improvement over the state-of-the-art.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5220/0010191200330042
