Detection of account cloning in online social networks

Name: Dechana Punkamol

Organization : Mahidol University. Department of Computer Engineering

Email : dechana.p@gmail.com

Name: Rangsipan Marukatat

Organization : Mahidol University. Department of Computer Engineering

Email : rangsipan.mar@mahidol.ac.th

keyword: User accounts (Computer systems)

LCSH: Online social networks

LCSH: Computer crimes

LCSH: Identity theft

LCSH: Intrusion detection systems (Computer security)

Abstract: This research proposes a framework to detect account cloning in online social networks. The main concept is to analyze user profiles, friend and follower networks, and user posting behaviors. The framework consists of 3 parts: Twitter Crawler, Attribute Extractor, and Cloning Detector. Twitter was used as a case study. Experimental results suggested that it was not easy to completely clone profiles and friend/follower networks of the victims. And even the attackers managed to do so or somehow fool other users, posting behaviors and writing styles could help distinguish between fake and authentic (i.e. done by the victims) posts. The average accuracy of classifying whether the posts were fake or authentic was 80%. Decision tree was found to yield the best classification performance.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2020

Modified: 2026-01-16

Issued: 2026-01-16

บทความ/Article

application/pdf

BibliograpyCitation : In Electrical Engineering Academic Association (Thailand). 2020 8th International Electrical Engineering Congress (iEECON 2020) (pp.338-341). Red Hook, NY : Institute of Electrical and Electronics Engineers

eng

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