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Computational Science and Its Applications – ICCSA 2010

Volume 6017 of the series Lecture Notes in Computer Science pp 351-360

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

  • Pedram HayatiAffiliated withCarnegie Mellon UniversityAnti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence Institute, Curtin University
  • , Kevin ChaiAffiliated withCarnegie Mellon UniversityAnti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence Institute, Curtin University
  • , Vidyasagar PotdarAffiliated withCarnegie Mellon UniversityAnti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence Institute, Curtin University
  • , Alex TalevskiAffiliated withCarnegie Mellon UniversityAnti-Spam Research Lab (ASRL) Digital Ecosystem and Business Intelligence Institute, Curtin University

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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