SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones

Wenjuan Li, Jiao Tan, Weizhi Meng, Yu Wang, Jing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

13 Citations (Scopus)


Smartphones have become a necessity in people’s daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.

Original languageEnglish
Title of host publicationMachine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings
EditorsXiaofeng Chen, Xinyi Huang, Jun Zhang
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030306182
Publication statusPublished - 2019
Externally publishedYes
Event2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019 - Xi'an, China
Duration: 19 Sept 201921 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11806 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019


  • Behavioral biometric
  • Smartphone security
  • Swipe behavior
  • Touch action
  • User authentication

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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