Users Feel Guilty: Measurement of Illegal Software Installation Guide Videos on YouTube for Malware Distribution
This study introduces and examines a sophisticated malware distribution technique that exploits popular video sharing platforms. In this attack, threat actors distribute malware through deceptive content that promises free versions of premium software and game cheats. Throughout this paper, we call...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | This study introduces and examines a sophisticated malware distribution
technique that exploits popular video sharing platforms. In this attack, threat
actors distribute malware through deceptive content that promises free versions
of premium software and game cheats. Throughout this paper, we call this attack
MalTube. MalTube is particularly insidious because it exploits the guilt
feelings of users for engaging in potentially illegal activity, making them
less likely to report the infection or ask for a help. To investigate this
emerging threat, we developed video platform exploitation reconnaissance VIPER,
a novel monitoring system designed to detect, monitor, and analyze MalTube
activity at scale. Over a four-month data collection period, VIPER processed
and analyzed 14,363 videos, 8,671 associated channels, and 1,269 unique fully
qualified domain names associated with malware downloads. Our findings reveal
that MalTube attackers primarily target young gamers, using the lure of free
software and game cheats as infection vectors. The attackers employ various
sophisticated social engineering techniques to maximize user engagement and
ensure successful malware propagation. These techniques include the strategic
use of platform-specific features such as trending keywords, emoticons, and
eye-catching thumbnails. These tactics closely mimic legitimate content
creation strategies while providing detailed instructions for malware
infection. Based on our in-depth analysis, we propose a set of robust detection
and mitigation strategies that exploit the invariant characteristics of MalTube
videos, offering the potential for automated threat detection and prevention. |
---|---|
DOI: | 10.48550/arxiv.2407.16132 |