Open AccessAn Enhanced Sybil Guard to Detect Bots in Online Social Networks
Author(s) -
Nisha P. Shetty,
Balachandra Muniyal,
Arshia Anand,
Sushant Kumar
Publication year - 2021
Publication title -
journal of cyber security and mobility
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 9
eISSN - 2245-4578
pISSN - 2245-1439
DOI - 10.13052/jcsm2245-1439.1115
Subject(s) - spamming , phishing , guard (computer science) , computer science , exploit , sybil attack , computer security , classifier (uml) , botnet , outreach , blacklist , crowdsourcing , social media , internet privacy , world wide web , artificial intelligence , the internet , computer network , wireless sensor network , programming language , political science , law
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.