Characterizing Robocalls with Multiple Vantage Points
Sathvik Prasad, Aleksandr Nahapetyan, Bradley Reaves

TL;DR
This study analyzes diverse data sources over five years to evaluate the effectiveness of robocall mitigation efforts, revealing slow decline in unsolicited calls but persistent high volumes and robocaller adaptation to authentication schemes.
Contribution
It introduces a methodology for comparing disparate data sources and provides a comprehensive, multi-perspective analysis of robocall trends and mitigation effectiveness.
Findings
Unsolicited calls are slowly declining.
Robocallers have adapted to STIR/SHAKEN.
Call volumes and complaints remain high.
Abstract
Telephone spam has been among the highest network security concerns for users for many years. In response, industry and government have deployed new technologies and regulations to curb the problem, and academic and industry researchers have provided methods and measurements to characterize robocalls. Have these efforts borne fruit? Are the research characterizations reliable, and have the prevention and deterrence mechanisms succeeded? In this paper, we address these questions through analysis of data from several independently-operated vantage points, ranging from industry and academic voice honeypots to public enforcement and consumer complaints, some with over 5 years of historic data. We first describe how we address the non-trivial methodological challenges of comparing disparate data sources, including comparing audio and transcripts from about 3 million voice calls. We also…
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Taxonomy
TopicsAdvanced Control Systems Optimization
