New robocall study shatters some myths
A recent robocall study report from researchers at North Carolina State University has shattered some myths about robocalls. The report describes the results of an 11-month study using a honeypot with over 60,000 numbers to log robocall activity.
The research study report lists 24 findings, listed below. Some of these were surprising and can help inform better robocall prevention tactics. Here are the notable highlights.
- Answering a robocall did not increase the number of robocalls received to a number. If you’re curious, go ahead and answer the call. Just be vigilant against fraudsters trying to scam you.
- The number of robocalls received remain steady over the 11-month study. This doesn’t match what other robocall prevention firms report and deserves further study.
- Most robocall campaigns made few calls. The average was 13 calls. 95% of the campaigns made fewer than 27 calls. This was an unexpected outcome. The tactic makes it harder to detect robocalls.
- Abuse numbers (e.g., numbers that had been reported as receiving many robocalls) received robocalls at the same rate as clean numbers. This also was a surprise. Robocallers don’t seem to be targeting certain numbers more than other, although there were some exceptions.
- Most robocall campaigns used many spoofed calling numbers with very little reuse. This also makes it harder to detect robocalls.
- Surprisingly, the study did not find a lot of neighbor spoofing. About 6% of robocalls spoofed a number in the same NPA, and 3% spoofed a number in the same NPA-NXX. Perhaps the smaller campaigns and large sets of spoofed numbers made neighbor spoofing too much trouble for the robocallers.
Here’s a comprehensive list of the research findings.
- Each inbound line received an average of 0.12 robocalls per day, or one call every 8.42 days.
- Clean numbers received robocalls at the same rate as numbers with a history of receiving excessive robocalls.
- Most inbound lines in the honeypot received robocalls. On average, it took 8 weeks for a number to receive the first robocall after being added to the honeypot.
- Robocall traffic was steady over the 11-month study.
- Most robocalls were received Monday through Friday.
- Most robocalls were received during local working hours, 9am–5pm.
- Researchers observed 648 robocall storms with unusually high robocall volume. The highest was 1,400 robocalls to the same called number on the same day. Researchers were unable to determine motives.
- Answering robocalls did not change the number of robocalls received.
- About 3% of all calls to the honeypot seemed designed to leave a recorded voicemail.
- Approximately 6% of robocalls used neighbor spoofing by matching NPA (or NPA-NXX).
- About 3% of robocalls could have been outright blocked by providers.
- 80% of robocalls that attempted to mask caller ID by using the *67 prefix were successful.
- Many called numbers were used in combination with a small pool of caller names.
- The study did not detect any Wangiri scam calls.
- 63% of calls did not have enough audio for clustering analysis.
- Of the calls with sufficient audio for clustering analysis, 63% were identified as belonging to a robocall campaign.
- 2,687 robocall campaigns were identified using clustering analysis.
- On average, a campaign made about 13 calls. Most campaigns were small in the number of calls made. The honeypot received fewer than 27 calls from 95% of the campaigns.
- Most robocall campaigns use a large pool of spoofed numbers as caller ID.
- Most campaigns target a wide range of called telephone numbers.
- About 3% of campaigns used neighbor spoofing by matching NPA and NXX.
- Researchers observed two campaigns that impersonated the U.S. Social Security Administration (SSA).
- SSA campaigns spoofed toll free calling numbers.
- Researchers observed two campaigns that targeted the Mandarin-speaking Chinese population. Both impersonated the Chinese Consulate and were designed to frighten called parties, so they’d call back.
The full report, Who’s Calling? Characterizing Robocalls through Audio and Metadata Analysis, is available online.
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