Evaluation of Deep Learning Models for Continuous Authentication Using Behavioral Biometrics

Utku Uslu, Özlem Durmaz Incel, Gülfem Isiklar Alptekin*

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

5 Citations (Scopus)
1 Downloads (Pure)

Abstract

The traditional method to authenticate users on mobile devices or applications requires usernames and passwords. This approach authenticates the user only at the entry point of an application without providing continuous authentication during the whole session. This paper explores the use of behavioral biometrics, which involves tracking the unique movements of a user while interacting with a device, for continuous authentication on a mobile banking application. As a methodology, we use binary classification and explore the performance of deep learning algorithms. A dataset is collected from 45 participants using a mobile banking application in Turkey. We train four different types of deep architectures, including Multilayer Perceptron (MLP), LSTM, bi-directional LSTM, and convolutional LSTM. The dataset includes data from both touch screens and motion sensors. The results of the experiments reveal that MLP and the convolutional-LSTM algorithms achieve the best performance on raw data from both motion sensors and touch screens. Accuracy rates are over 99.85%, and FAR, FRR, and EER are below 0.5%.

Original languageEnglish
Pages (from-to)1272-1281
Number of pages10
JournalProcedia computer science
Volume225
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023 - Athens, Greece
Duration: 6 Sept 20238 Sept 2023
Conference number: 27

Keywords

  • behavioral biometrics
  • Continuous authentication
  • deep learning
  • mobile banking

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