Utilizing sequences of touch gestures for user verification on mobile devices

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Smartphones have become ubiquitous in our daily lives; they are used for a wide range of tasks and store increasing amounts of personal data. To minimize risk and prevent misuse of this data by unauthorized users, access must be restricted to verified users. Current classification-based methods for gesture-based user verification only consider single gestures, and not sequences. In this paper, we present a method which utilizes information from sequences of touchscreen gestures, and the context in which the gestures were made using only basic touch features. To evaluate our approach, we built an application which records all the necessary data from the device (touch and contextual sensors which do not consume significant battery life). Using XGBoost on the collected data, we were able to classify between a legitimate user and the population of illegitimate users (imposters) with an average equal error rate (EER) of 4.78% and an average area under the curve (AUC) of 98.15%. Our method demonstrates that by considering only basic touch features and utilizing sequences of gestures, as opposed to individual gestures, the accuracy of the verification process improves significantly.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
PublisherSpringer Verlag
Pages816-828
Number of pages13
ISBN (Electronic)978-3-319-93040-4
ISBN (Print)9783319930398
DOIs
StatePublished - 17 Jun 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Country/TerritoryAustralia
CityMelbourne
Period3/06/186/06/18

Keywords

  • Behavioral models
  • Context
  • Continuous user verification
  • Mobile
  • Security
  • Sequence recognition
  • Touchscreen gestures
  • XGBoost

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Utilizing sequences of touch gestures for user verification on mobile devices'. Together they form a unique fingerprint.

Cite this