Fake Profile Detection and Stalking Prediction on Facebook

Swathi, Mummadi; Anoop, Ashley; Rudra, Bhawana

doi:10.1007/978-981-16-1740-9_2

Cited by 4 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their paper, Prabhu Kavin et al 126 proposed an AI technique using a support vector machine and ANN algorithms to detect fake accounts and spam content in social media. Analyzing various characteristics, Sahoo and Gupta 172 discussed a chrome extension-based framework for detecting fake accounts in the Twitter environment [173][174][175] also used ML based approach to detect fake or clone accounts in social networks.…”

Section: Solutions For Profile Cloning Attacksmentioning

confidence: 99%

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

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

View full text Add to dashboard Cite

Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

show abstract