Measuring and Modeling the Free Content Web

• URL: http://arxiv.org/abs/2304.14359v1
• Date: Wed, 26 Apr 2023 04:17:43 GMT
• Title: Measuring and Modeling the Free Content Web
• Authors: Abdulrahman Alabduljabbar and Runyu Ma and Ahmed Abusnaina and Rhongho Jang and Songqing Chen and DaeHun Nyang and and David Mohaisen
• Abstract summary: We investigate the similarities and differences between free content and premium websites. For risk analysis, we consider and examine the maliciousness of these websites at the website- and component-level.
• Score: 13.982229874909978
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Free content websites that provide free books, music, games, movies, etc., have existed on the Internet for many years. While it is a common belief that such websites might be different from premium websites providing the same content types, an analysis that supports this belief is lacking in the literature. In particular, it is unclear if those websites are as safe as their premium counterparts. In this paper, we set out to investigate, by analysis and quantification, the similarities and differences between free content and premium websites, including their risk profiles. To conduct this analysis, we assembled a list of 834 free content websites offering books, games, movies, music, and software, and 728 premium websites offering content of the same type. We then contribute domain-, content-, and risk-level analysis, examining and contrasting the websites' domain names, creation times, SSL certificates, HTTP requests, page size, average load time, and content type. For risk analysis, we consider and examine the maliciousness of these websites at the website- and component-level. Among other interesting findings, we show that free content websites tend to be vastly distributed across the TLDs and exhibit more dynamics with an upward trend for newly registered domains. Moreover, the free content websites are 4.5 times more likely to utilize an expired certificate, 19 times more likely to be malicious at the website level, and 2.64 times more likely to be malicious at the component level. Encouraged by the clear differences between the two types of websites, we explore the automation and generalization of the risk modeling of the free content risky websites, showing that a simple machine learning-based technique can produce 86.81\% accuracy in identifying them.

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