An Enhanced Sybil Guard to Detect Bots in Online Social Networks

Authors

  • Nisha P. Shetty Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India https://orcid.org/0000-0002-4738-4713
  • Balachandra Muniyal Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India https://orcid.org/0000-0002-4839-0082
  • Arshia Anand Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India https://orcid.org/0000-0002-4303-7378
  • Sushant Kumar Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India https://orcid.org/0000-0001-9711-0166

DOI:

https://doi.org/10.13052/jcsm2245-1439.1115

Keywords:

Sybil Guard, Random Walks (RW), Sybil, Loop Belief Propagation, behavior based detection, Machine Learning

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.

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Author Biographies

Nisha P. Shetty, Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India

Nisha P. Shetty has acquired her bachelor and master’s degree from Visvesvaraya Technological University. She is currently pursuing her doctorate at Manipal Institute of Technology, Manipal. She is working in the area of social network security.

Balachandra Muniyal, Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India

Balachandra Muniyal’s research area includes Network Security, Algorithms, and Operating systems. He has more than 30 publications in national and international conferences/journals. Currently he is working as the Professor in the Dept. of Information & Communication Technology, Manipal Institute of Technology, Manipal. He has around 25 years of teaching experience in various Institutes.

Arshia Anand, Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India

Arshia Anand has pursued her bachelor’s degree in Computer and Communication Engineering branch from Manipal Institute of Technology, Manipal. Her areas of interest include Data Science and Full Stack Development. She is currently working as an Analyst in Goldman Sachs.

Sushant Kumar, Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-567104, India

Sushant Kumar has pursued her bachelor’s degree in Computer and Communication Engineering branch from Manipal Institute of Technology, Manipal. His areas of interests are Data Science and full-stack development. He is currently working in GE Renewable Energy as a Software Engineer.

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Published

2021-11-20

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