Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System

Pei Yuan Wu, Chi Chen Fang, Jien Morris Chang, Sun Yuan Kung

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


In this paper, a fast kernel ridge regression (KRR) learning algorithm is adopted with {O} ( {N} ) training cost for large-scale active authentication system. A truncated Gaussian radial basis function (TRBF) kernel is also implemented to provide better cost-performance tradeoff. The fast-KRR algorithm along with the TRBF kernel offers computational advantages over the traditional support vector machine (SVM) with Gaussian-RBF kernel while preserving the error rate performance. Experimental results validate the cost-effectiveness of the developed authentication system. In numbers, the fast-KRR learning model achieves an equal error rate (EER) of 1.39% with {O} ( {N} ) training time, while SVM with the RBF kernel shows an EER of 1.41% with {O} (N) 2) training time.

Original languageEnglish (US)
Article number7529146
Pages (from-to)3916-3927
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number11
StatePublished - Nov 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


  • Active authentication
  • cost-effective
  • kernel methods
  • kernel ridge regression (KRR)
  • keystroke
  • support vector machine (SVM)
  • truncated-radial basis function (TRBF)


Dive into the research topics of 'Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System'. Together they form a unique fingerprint.

Cite this