Detecting Compromised Social Network Accounts Using Deep Learning for Behavior and Text Analyses

Autor:	Steven Yen, Melody Moh, Teng-Sheng Moh
Rok vydání:	2021
Předmět:	Social network business.industry Computer science Deep learning Compromise media_common.quotation_subject Online identity Cloud computing Computer security computer.software_genre Perceptron Phishing Autoencoder Artificial intelligence business computer media_common
Zdroj:	International Journal of Cloud Applications and Computing. 11:97-109
ISSN:	2156-1826 2156-1834
Popis:	Social networks allow people to connect to one another. Over time, these accounts become an essential part of one’s online identity. The account stores various personal data and contains one’s network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected. Copyright © 2021, IGI Global.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f2b3bdc079802c10dba029286babb4a4 https://doi.org/10.4018/ijcac.2021040106 Zobrazit plný text záznamu