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Recognizing User Portraits for Fraudulent Identification on Online Social Networks
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Author(s): Sudha Senthilkumar (School of Computer Science and Engineering, VIT University, Vellore, India), Satha Sivam S. (School of Information Technology and Engineering, VIT University, Vellore, India)and Brindha K. (School of Information Technology and Engineering, VIT University, Vellore, India)
Copyright: 2022
Pages: 15
Source title:
Methods, Implementation, and Application of Cyber Security Intelligence and Analytics
Source Author(s)/Editor(s): Jena Om Prakash (Ravenshaw University, India), H.L. Gururaj (Vidyavardhaka College of Engineering, India), M.R. Pooja (Vidyavardhaka College of Engineering, India)and S.P. Pavan Kumar (Vidyavardhaka College of Engineering, India)
DOI: 10.4018/978-1-6684-3991-3.ch013
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Abstract
Online social networks (OSNs) are increasingly influencing the way people communicate with each other. Well known sites such as Facebook, LinkedIn, Twitter, and Google+ have millions of users across the globe. With the wide popularity there are lot of security and privacy threats to the users of online social networks (OSN) such as breach of privacy, viral marketing, structural attacks, malware attacks, and profile cloning. Social networks have permitted people to have their own virtual identities which they use to interact with other online users. It is also completely possible and not uncommon for a user to have more than one online profile or even a completely different anonymous online identity. Entity resolution (ER) is the task of matching two different online profiles potentially from social networks. Solving ER has an identification of fake profiles. The solution compares profiles based on similar attributes. The system was tasked with matching two profiles that were in a pool of extremely similar profiles.
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