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A machine learning framework to detect and document text-based cyberstalking

  • Zinnar Ghasem
    ,
  • Ingo Frommholz
    ,
  • Carsten Maple
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

Sustainable Development Goals

  • SDG 16 - Peace, Justice and Strong Institutions
    SDG 16 Peace, Justice and Strong Institutions

Abstract

Cyberstalking is becoming a social and international problem, where cyberstalkers utilise the Internet to target individuals and disguise themselves without fear of any consequences. Several technologies, methods, and techniques are used by perpetrators to terrorise victims. While spam email filtering systems have been effective by applying various statistical and machine learning algorithms, utilising text categorization and filtering to detect text- and email-based cyberstalking is an interesting new application. There is also the need to gather evidence by the victim. To this end we discuss a framework to detect cyberstalking in messages; short message service, multimedia messaging service, chat, instance messaging and emails, and as well as to support documenting evidence. Our framework consists of five main modules: a detection module which detects cyberstalking using message categorisation; an attacker identification module based on cyberstalkers' previous messages history, personalisation module, aggregator module and messages and evidence collection module. We discuss our ongoing work and how different text categorization and machine learning approaches can be applied to identify cyberstalkers.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 348-355

Publication milestones

  • Published - 31/12/2015

Publication status

Published - 31/12/2015

Edition

Vol 1458

Volume

1458

Publisher

CEUR-WS, Germany

Publication IDs

  • handle.net: 10547/624261
  • Scopus: 84944342737

Host publication title

CEUR Workshop Proceedings

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