Social Media Identity Deception Detection: A Survey

Autor:	Alharbi, Ahmed, Dong, Hai, Yi, Xun, Tari, Zahir, Khalil, Ibrahim
Rok vydání:	2021
Předmět:	Computer Science - Cryptography and Security Computer Science - Social and Information Networks
Zdroj:	ACM Computing Surveys (CSUR), 54(3), 1-35 (2021)
Druh dokumentu:	Working Paper
DOI:	10.1145/3446372
Popis:	Social media have been growing rapidly and become essential elements of many people's lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyses various identity deception attacks, which can be categorized into fake profile, identity theft and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers. Comment: Accepted for publication in ACM Computing Surveys
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.04673 Zobrazit plný text záznamu View this record from Arxiv