An Enhanced Sybil Guard to Detect Bots in Online Social Networks

Nisha P. Shetty, Balachandra Muniyal, Arshia Anand, Sushant Kumar

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

Original languageEnglish
Pages (from-to)105-126
Number of pages22
JournalJournal of Cyber Security and Mobility
Volume11
Issue number1
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications

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