Capturing Cognitive Processing Time for Active Authentication

Abstract

This report presents an authentication system that applies machine learning techniques to observe a user s cognitive typing rhythm. A new feature called cognitive typing rhythm (CTR) is used to continuously verify the identities of computer users. Two machine techniques, SVM and KRR, have been developed for the system. The best results from experiments conducted with 1,977 users show a false-rejection rate of 0.7 percent and a false-acceptance rate of 5.5 percent. CTR therefore constitutes a cognitive fingerprint for continuous authentication. Its effectiveness has been verified through a campus-wide experiment at Iowa State University. Furthermore, a live demo was performed twice to demonstrate the effectiveness of our system.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2014
Accession Number
ADA599700

Entities

People

  • Jien Chang

Organizations

  • Iowa State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Authentication
  • Biometric Security
  • Biometrics
  • Cognition
  • Computers
  • Detection
  • Dimensionality Reduction
  • Fingerprints
  • Government Procurement
  • Governments
  • Information Science
  • Learning
  • Machine Learning
  • Rejection
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Computer Vision.
  • Database Systems and Applications

Technology Areas

  • AI & ML