An Enhanced Sybil Guard to Detect Bots in Online Social Networks

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.

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

“…Recently, Shetty et al 136 developed a new classifier by combining Profile statistics, Sybil Guard, and Twitter engagement rate, which can detect Sybil accounts more accurately. Mao et al 137 proposed a hybrid graph‐based Sybil node detection method “SybilHunter.” Based on the dynamic behavior of OSN users, it will detect Sybil accounts.…”

A Comparative Analysis of Digital Forensic Investigation Tools on Facebook Messenger Applications

“…rank both in top ten downloaded apps or frequently visited sites. Simplicity, with no cost account creation and usage, has attracted masses in huge numbers towards these sites [2].…”