Key stroke profiling for data loss prevention

Jain Shing Wu, Yuh-Jye Lee, Song Kong Chong, Chih Ta Lin, Jung Lun Hsu

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Data leakage has become a serious problem to many organizations. To provide visibility into what data is confidential and where it's stored, many of current data leakage prevention (DLP) solutions depend on scanning file content. This approach needs the capability of parsing various file formats, but for those unsupported file formats there still exist risks of data breach. To address this issue, this study proposes an active DLP model by hooking on keyboard API to track and profile user key stroke behaviour. This has two major advantages: (1) It can discover sensitive data without parsing file formats, and (2) A data creator can be identified according to his/her key stroke behaviour. Since this model is based on key stroke profiling, it can resolve unsupported file format issue and have the capability of file creator identification.

Original languageEnglish
Pages7-12
Number of pages6
DOIs
StatePublished - 1 Jan 2013
Event2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, Taiwan
Duration: 6 Dec 20138 Dec 2013

Conference

Conference2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013
CountryTaiwan
CityTaipei
Period6/12/138/12/13

Keywords

  • Data leakage prevention
  • File parser
  • Key stroke profiling
  • Sensitive data protection

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