An Overview of 7726 User Reports: Uncovering SMS Scams and Scammer Strategies
Sharad Agarwal, Guillermo Suarez-Tangil, Marie Vasek

TL;DR
This study analyzes 1.35 million user reports to distinguish between spam and scam SMS messages, revealing scam strategies, infrastructure abuse, and the most common scam types, providing new insights into SMS scam evasion tactics.
Contribution
First comprehensive analysis of user-reported SMS messages that differentiates spam from scams and classifies scam types, revealing scammer infrastructure and tactics.
Findings
89.16% of reports are text messages
35.12% of unique messages are spam
40.27% are scam messages
Abstract
Mobile network operators implement firewalls to stop illicit messages, but scammers find ways to evade detection. Previous work has looked into SMS texts that are blocked by these firewalls. However, there is little insight into SMS texts that bypass them and reach users. To this end, we collaborate with a major mobile network operator to receive 1.35m user reports submitted over four months. We find 89.16% of user reports comprise text messages, followed by reports of suspicious calls and URLs. Using our methodological framework, we identify 35.12% of the unique text messages reported by users as spam, while 40.27% are scam text messages. This is the first paper that investigates SMS reports submitted by users and differentiates between spam and scams. Our paper classifies the identified scam text messages into 12 scam types, of which the most popular is 'wrong number' scams. We…
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