Touch based active user authentication using deep belief networks and random forests

Ye Seon Lee, William Hetchily, Joseph Shelton, Dylan Gunn, Kaushik Roy, Albert Esterline, Xiaohong Yuan

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

Abstract

While mobile devices traditionally use authentication methods such as passwords that define a single point of entry, active authentication can provide greater security by continuously authenticating users while they use the device. By extracting features based on users' interaction with the touchscreen, we can distinguish between different users. In this research, we investigate the performances of Deep Belief Networks (DBN) and Random Forest (RF), a more traditional classification algorithm, to classify users using a dataset extracted from the touch patterns of 41 users. The dataset is separated into strokes, which are then grouped into sessions. The preliminary results show that DBNs are outperformed by the RF.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Information Communication and Management, ICICM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages304-308
Number of pages5
ISBN (Electronic)9781509034949
DOIs
StatePublished - Dec 14 2016
Externally publishedYes
Event6th International Conference on Information Communication and Management, ICICM 2016 - Hatfield, Hertfordshire, United Kingdom
Duration: Oct 29 2016Oct 31 2016

Publication series

NameProceedings of the 6th International Conference on Information Communication and Management, ICICM 2016

Conference

Conference6th International Conference on Information Communication and Management, ICICM 2016
Country/TerritoryUnited Kingdom
CityHatfield, Hertfordshire
Period10/29/1610/31/16

Keywords

  • Active authentication
  • deep belief networks
  • random forest

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