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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationMachine Learning for Cyber Security
Subtitle of host publicationProceedings
EditorsXiaofeng Chen, Xinyi Huang, Jun Zhang
PublisherSpringer Nature Switzerland AG
Pages140-153
ISBN (Electronic)9783030306199
ISBN (Print)9783030306182
Publication statusPublished - Sep 2019

Publication series

NameLecture Notes in Computer Science
Volume11806 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title2nd International Conference on Machine Learning for Cyber Security (ML4CS 2019)
PlaceChina
CityXi'an
Period19 - 21 September 2019

Abstract

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.

Research Area(s)

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

Citation Format(s)

SwipeVLock : A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones. / Li, Wenjuan; Tan, Jiao; Meng, Weizhi; Wang, Yu; Li, Jing.

Machine Learning for Cyber Security: Proceedings. ed. / Xiaofeng Chen; Xinyi Huang; Jun Zhang. Springer Nature Switzerland AG, 2019. p. 140-153 (Lecture Notes in Computer Science; Vol. 11806 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)