View in EDS HTML Full Text PDF Full Text

Cross-device free-text keystroke dynamics authentication using federated learning.

Saved in:
Bibliographic Details
Title: Cross-device free-text keystroke dynamics authentication using federated learning.
Authors: Yang, Yafang1 (AUTHOR), Guo, Bin1 (AUTHOR) guob@nwpu.edu.cn, Liang, Yunji1 (AUTHOR), Zhao, Kaixing1 (AUTHOR), Yu, Zhiwen1 (AUTHOR)
Source: Personal & Ubiquitous Computing. Aug2024, Vol. 28 Issue 3/4, p491-505. 15p.
Subjects: Federated learning, Keyboarding, Rhythm, Privacy
Abstract: Free-text keystroke dynamics, the unique typing patterns of an individual, have been applied for the security of mobile devices by providing the non-intrusive and continuous user authentication. Existing authentication approaches mainly concentrate on the keystroke dynamics when operating a specific device, and overlook the generality of keystroke dynamics for cross-device user authentication. To tackle this problem, in this paper, we propose an efficient federated free-text keystroke dynamics mechanism to mitigate the difference in keyboards for cross-device authentication. Specifically, we explore and analyze the keystroke features of various keyboards and extract cross-device keystroke features. To protect user privacy, their type of rhythm information must be kept locally. We utilize federated learning based on the auxiliary model to train the authentication model. Our proposed solution was evaluated on a large-scale data set with 168,000 users. The experimental results show that our proposed solution performs well with great robustness across different types of keyboards. [ABSTRACT FROM AUTHOR]
Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 179711598
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Cross-device free-text keystroke dynamics authentication using federated learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yafang%22">Yang, Yafang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Bin%22">Guo, Bin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guob@nwpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Yunji%22">Liang, Yunji</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Kaixing%22">Zhao, Kaixing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Zhiwen%22">Yu, Zhiwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Personal+%26+Ubiquitous+Computing%22">Personal & Ubiquitous Computing</searchLink>. Aug2024, Vol. 28 Issue 3/4, p491-505. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Keyboarding%22">Keyboarding</searchLink><br /><searchLink fieldCode="DE" term="%22Rhythm%22">Rhythm</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Free-text keystroke dynamics, the unique typing patterns of an individual, have been applied for the security of mobile devices by providing the non-intrusive and continuous user authentication. Existing authentication approaches mainly concentrate on the keystroke dynamics when operating a specific device, and overlook the generality of keystroke dynamics for cross-device user authentication. To tackle this problem, in this paper, we propose an efficient federated free-text keystroke dynamics mechanism to mitigate the difference in keyboards for cross-device authentication. Specifically, we explore and analyze the keystroke features of various keyboards and extract cross-device keystroke features. To protect user privacy, their type of rhythm information must be kept locally. We utilize federated learning based on the auxiliary model to train the authentication model. Our proposed solution was evaluated on a large-scale data set with 168,000 users. The experimental results show that our proposed solution performs well with great robustness across different types of keyboards. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Personal & Ubiquitous Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=179711598
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00779-024-01832-6
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 491
    Subjects:
      – SubjectFull: Federated learning
        Type: general
      – SubjectFull: Keyboarding
        Type: general
      – SubjectFull: Rhythm
        Type: general
      – SubjectFull: Privacy
        Type: general
    Titles:
      – TitleFull: Cross-device free-text keystroke dynamics authentication using federated learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yang, Yafang
      – PersonEntity:
          Name:
            NameFull: Guo, Bin
      – PersonEntity:
          Name:
            NameFull: Liang, Yunji
      – PersonEntity:
          Name:
            NameFull: Zhao, Kaixing
      – PersonEntity:
          Name:
            NameFull: Yu, Zhiwen
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: Aug2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 16174909
          Numbering:
            – Type: volume
              Value: 28
            – Type: issue
              Value: 3/4
          Titles:
            – TitleFull: Personal & Ubiquitous Computing
              Type: main
ResultId 1