Detecting compromised social network accounts using deep learning for behavior and text analyses

Publication Date

4-1-2021

Document Type

Article

Publication Title

International Journal of Cloud Applications and Computing

Volume

11

Issue

2

DOI

10.4018/IJCAC.2021040106

First Page

97

Last Page

109

Abstract

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.

Keywords

Anomaly Score Algorithm, Autoencoder (AE), Behavioral Information, Mean-Squared-Error (MSE), Multi-Layer Perceptron (MLP), Natural Language Processing (NLP), Online Attacks, Textural Information

Department

Computer Science

Link to Full Text

Share

COinS