Geometrical analysis of machine learning security in biometric authentication systems

Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, Sandeep Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Feature extraction and Machine Learning (ML) techniques are required to reduce high variability of biometric data in Biometric Authentication Systems (BAS) toward improving system utilization (acceptance of legitimate subjects). However, reduction in data variability, also decreases the adversary's effort in manufacturing legitimate biometric data to break the system (security strength). Typically for BAS design, security strength is evaluated through variability analysis on data, regardless of feature extraction and ML, which are essential for accurate evaluation. In this research, we provide a geometrical method to measure the security strength in BAS, which analyzes the effects of feature extraction and ML on the biometric data. Using the proposed method, we evaluate the security strength of five state-of-the-art electroencephalogram-based authentication systems, on data from 106 subjects, and the maximum achievable security strength is 83 bits.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-314
Number of pages6
ISBN (Electronic)9781538614174
DOIs
StatePublished - 2017
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: Dec 18 2017Dec 21 2017

Publication series

NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume2017-December

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/TerritoryMexico
CityCancun
Period12/18/1712/21/17

Keywords

  • geometrical analysis
  • machine learning
  • security

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Geometrical analysis of machine learning security in biometric authentication systems'. Together they form a unique fingerprint.

Cite this