Automatic Detection and Analysis of the “Game Hack” Scam
Badawi, Emad ; Jourdan, Guy-Vincent ; Bochmann, Gregor ; Onut, Iosif-Viorel (2020) — Journal of Web Engineering
Type:
Journal Article
Country:
Canada
Tags:
victim experience, AI misuse, measurement
Methods:
survey
AI-Generated Synopsis
The Game Hack Scam (GHS) is characterized as a largely unreported cyberattack in which offenders lure victims with promises of free, unlimited resources or other advantages for their preferred game. The attackers’ end goals range from monetizing victims’ time and resources through endless surveys and purported market research tasks to harvesting personal information, prompting subscriptions to questionable services, or installing dubious executable files on the victims’ devices. Although related scams such as Technical Support Scam, Survey Scam, and Romance Scam have received prior attention, GHS has not been thoroughly studied. This work adopts a data-driven approach to expand understanding of this phenomenon by formulating GHS–related search queries and employing multiple search engines to collect data about the websites victims are directed to when seeking game hacks and tips. The analysis seeks to characterize the breadth and mechanics of GHS and to quantify its reach. Findings indicate that, despite