Ctrl+Alt+Deceive: Quantifying User Exposure to Online Scams
Platon Kotzias, Michalis Pachilakis, Javier Aldana-Iuit, Juan Caballero, Iskander Sánchez-Rola, Leyla Bilge
摘要
—Online scams have become a top threat for Internet users, inflicting $10 billion in losses in 2023 only in the US. Prior work has studied specific scam types, but no work has compared different scam types. In this work, we perform what we believe is the first study of the exposure of end users to different types of online scams. We examine seven popular scam types: shopping, financial, cryptocurrency, gambling, dating, funds recovery, and employment scams. To quantify end-user exposure, we search for observations of 607K scam domains over a period of several months by millions of desktop and mobile devices belonging to customers of a large cybersecurity vendor. We classify the scam domains into the seven scam types and measure for each scam type the exposure of end users, geographical variations, scam domain lifetime, and the promotion of scam websites through online advertisements. We examine 25.1M IP addresses accessing over 414K scam domains. On a daily basis, 149K devices are exposed to online scams, with an average of 101K (0.8%) of desktop devices being exposed compared to 48K (0.3%) of mobile devices. Shopping scams are the most prevalent scam type, being observed by a total of 10.2M IPs, followed by cryptocurrency scams, observed by 653K IPs. After being observed in the telemetry, the scam domains remain alive for a median of 11 days. In at least 9.2M (13.3%) of all scam observations users followed an advertisement. These ads are largely (59%) hosted on social media, with Facebook being the preferred source.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper13
- Tracking Ransomware End-to-endDanny Yuxing Huang, Maxwell Matthaios Aliapoulios, Vector Guo Li, Luca Invernizzi 等S&P 2018 · 被引用 208 次
- Understanding Security Issues in the NFT EcosystemDipanjan Das, Priyanka Bose, Nicola Ruaro, Christopher Kruegel 等CCS 2022 · 被引用 173 次
- Surveylance: Automatically Detecting Online Survey ScamsAmin Kharraz, William K. Robertson, Engin KirdaS&P 2018 · 被引用 73 次
- Demystifying Illegal Mobile Gambling AppsYuhao Gao, Haoyu Wang, Li Li, Xiapu Luo 等WWW 2021 · 被引用 30 次
- Conning the Crypto Conman: End-to-End Analysis of Cryptocurrency-based Technical Support ScamsBhupendra Acharya, Muhammad Saad, Antonio Emanuele Cinà, Lea Schönherr 等S&P 2024 · 被引用 26 次
相关 Paper
- The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment ScamsMuhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati 等WWW 2025 · 被引用 13 次
- Analyzing Ground-Truth Data of Mobile Gambling ScamsGeng Hong, Zhemin Yang, Sen Yang, Xiaojing Liao 等S&P 2022 · 被引用 29 次
- Dial One for Scam: A Large-Scale Analysis of Technical Support ScamsNajmeh Miramirkhani, Oleksii Starov, Nick NikiforakisNDSS 2017 · 被引用 116 次
- Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway ScamsXigao Li, Anurag Yepuri, Nick NikiforakisNDSS 2023
- Like, Comment, Get Scammed: Characterizing Comment Scams on Media PlatformsXigao Li, Amir Rahmati, Nick NikiforakisNDSS 2024
