Who's Calling? Characterizing Robocalls through Audio and Metadata Analysis

Sathvik Prasad, Elijah Bouma-Sims, Athishay Kiran Mylappan, and Bradley Reaves

Proceedings of the USENIX Security Symposium, 2020

A 66,000 line phone honeypot finds no evidence that answering robocalls increases future call volume, overturning popular wisdom.

Abstract

Unsolicited calls are one of the most prominent security issues facing individuals today. Despite wide-spread anecdotal discussion of the problem, many important questions remain unanswered. In this paper, we present the first largescale, longitudinal analysis of unsolicited calls to a honeypot of up to 66,606 lines over 11 months. From call metadata we characterize the long-term trends of unsolicited calls, develop the first techniques to measure voicemail spam, wangiri attacks, and identify unexplained high-volume call incidences. Additionally, we mechanically answer a subset of the call attempts we receive to cluster related calls into operational campaigns, allowing us to characterize how these campaigns use telephone numbers. Critically, we find no evidence that answering unsolicited calls increases the amount of unsolicited calls received, overturning popular wisdom. We also find that we can reliably isolate individual call campaigns, in the process revealing the extent of two distinct Social Security scams while empirically demonstrating the majority of campaigns rarely reuse phone numbers. These analyses comprise powerful new tools and perspectives for researchers, investigators, and a beleaguered public.

Citation (IEEE)

S. Prasad, E. Bouma-Sims, A. K. Mylappan, and B. Reaves, “Who’s Calling? Characterizing Robocalls through Audio and Metadata Analysis,” in Proceedings of the USENIX Security Symposium, 2020.

BibTeX

@inproceedings{pbmr20,
  author = {{Sathvik Prasad} and {Elijah Bouma-Sims} and {Athishay Kiran Mylappan} and {Bradley Reaves}},
  booktitle = {Proceedings of the {USENIX} Security Symposium},
  date = {2020-08},
  title = {Who's Calling? {C}haracterizing Robocalls through Audio and Metadata Analysis},
}