CABA: Continuous Authentication Based on BioAura

Arsalan Mosenia, Susmita Sur-Kolay, Anand Raghunathan, Niraj K. Jha

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Most computer systems authenticate users only once at the time of initial login, which can lead to security concerns. Continuous authentication has been explored as an approach for alleviating such concerns. Previous methods for continuous authentication primarily use biometrics, e.g., fingerprint and face recognition, or behaviometrics, e.g., key stroke patterns. We describe CABA, a novel continuous authentication system that is inspired by and leverages the emergence of sensors for pervasive and continuous health monitoring. CABA authenticates users based on their BioAura, an ensemble of biomedical signal streams that can be collected continuously and non-invasively using wearable medical devices. While each such signal may not be highly discriminative by itself, we demonstrate that a collection of such signals, along with robust machine learning, can provide high accuracy levels. We demonstrate the feasibility of CABA through analysis of traces from the MIMIC-II dataset. We propose various applications of CABA, and describe how it can be extended to user identification and adaptive access control authorization. Finally, we discuss possible attacks on the proposed scheme and suggest corresponding countermeasures.

Original languageEnglish (US)
Article number7723816
Pages (from-to)759-772
Number of pages14
JournalIEEE Transactions on Computers
Issue number5
StatePublished - May 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


  • Authentication
  • behaviometrics
  • biomedical signals
  • biometrics
  • biostreams
  • continuous authentication
  • machine learning
  • security
  • wearable medical devices


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