Behaviour-Based Web Spambot Detection by Utilising Action Time and Action Frequency

  • Pedram Hayati
  • Kevin Chai
  • Vidyasagar Potdar
  • Alex Talevski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

Web spam is an escalating problem that wastes valuable resources, misleads people and can manipulate search engines in achieving undeserved search rankings to promote spam content. Spammers have extensively used Web robots to distribute spam content within Web 2.0 platforms. We referred to these web robots as spambots that are capable of performing human tasks such as registering user accounts as well as browsing and posting content. Conventional content-based and link-based techniques are not effective in detecting and preventing web spambots as their focus is on spam content identification rather than spambot detection. We extend our previous research by proposing two action-based features sets known as action time and action frequency for spambot detection. We evaluate our new framework against a real dataset containing spambots and human users and achieve an average classification accuracy of 94.70%.

Keywords

Web spambot detection Web 2.0 spam spam 2.0 user behaviour 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pedram Hayati
    • 1
  • Kevin Chai
    • 1
  • Vidyasagar Potdar
    • 1
  • Alex Talevski
    • 1
  1. 1.Anti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence InstituteCurtin UniversityPerthWestern Australia

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