Dracula
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The latest weapon in the war on robocalls is an automated system that analyzes the content of these unsolicited bulk calls to shed light on both the scope of the problem and the type of scams being perpetuated by robocalls. The tool, called SnorCall, is designed to help regulators, phone carriers and other stakeholders better understand and monitor robocall trends -- and take action against related criminal activity.
"Although telephone service providers, regulators and researchers have access to call metadata -- such as the number being called and the length of the call -- they do not have tools to investigate what is being said on robocalls at the vast scale required," says Brad Reaves, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.
"For one thing, providers don't want to listen in on calls -- it raises significant privacy concerns. But robocalls are a huge problem, and are often used to conduct criminal fraud. To better understand the scope of this problem, and gain insights into these scams, we need to know what is being said on these robocalls.
"We've developed a tool that allows us to the characterize the content of robocalls," Reaves says. "And we've done it without violating privacy concerns; in collaboration with a telecommunications company called Bandwidth, we operate more than 60,000 phone numbers that are used solely by us to monitor unsolicited robocalls. We did not use any phone numbers of actual customers."
The new tool, SnorCall, essentially records all robocalls received on the monitored phone lines. It bundles together robocalls that use the same audio, reducing the number of robocalls whose content needs to be analyzed by around an order of magnitude. These recorded robocalls are then transcribed and analyzed by a machine learning framework called Snorkel that can be used to characterize each call.
"SnorCall essentially uses labels to identify what each robocall is about," Reaves says. "Does it mention a specific company or government program? Does it request specific personal information? If so, what kind? Does it request money? If so, how much? This is all fed into a database that we can use to identify trends or behaviors."
As a proof of concept, the researchers used SnorCall to assess 232,723 robocalls collected over 23 months on the more than 60,000 phone lines dedicated to the study.
The latest weapon in the war on robocalls is an automated system that analyzes the content of these unsolicited bulk calls to shed light on both the scope of the problem and the type of scams being perpetuated by robocalls. The tool, called SnorCall, is designed to help regulators, phone carriers and other stakeholders better understand and monitor robocall trends -- and take action against related criminal activity.
"Although telephone service providers, regulators and researchers have access to call metadata -- such as the number being called and the length of the call -- they do not have tools to investigate what is being said on robocalls at the vast scale required," says Brad Reaves, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.
"For one thing, providers don't want to listen in on calls -- it raises significant privacy concerns. But robocalls are a huge problem, and are often used to conduct criminal fraud. To better understand the scope of this problem, and gain insights into these scams, we need to know what is being said on these robocalls.
"We've developed a tool that allows us to the characterize the content of robocalls," Reaves says. "And we've done it without violating privacy concerns; in collaboration with a telecommunications company called Bandwidth, we operate more than 60,000 phone numbers that are used solely by us to monitor unsolicited robocalls. We did not use any phone numbers of actual customers."
The new tool, SnorCall, essentially records all robocalls received on the monitored phone lines. It bundles together robocalls that use the same audio, reducing the number of robocalls whose content needs to be analyzed by around an order of magnitude. These recorded robocalls are then transcribed and analyzed by a machine learning framework called Snorkel that can be used to characterize each call.
"SnorCall essentially uses labels to identify what each robocall is about," Reaves says. "Does it mention a specific company or government program? Does it request specific personal information? If so, what kind? Does it request money? If so, how much? This is all fed into a database that we can use to identify trends or behaviors."
As a proof of concept, the researchers used SnorCall to assess 232,723 robocalls collected over 23 months on the more than 60,000 phone lines dedicated to the study.
"For example, if investigators want to focus on a new scam topic, Snorkel is very good at identifying key terms or phrases associated with topics," Reaves says. "This could be a valuable feature for investigators who are focused on specific types of criminal fraud."
"Our findings demonstrate how illegal robocalls use major societal events like student loan forgiveness to develop new types of scams," says Sathvik Prasad, a Ph.D. student at NC State and first author of the paper. "SnorCall can aid stakeholders to monitor well-known robocall categories and also help them uncover new types of robocalls."
"There's no way we could have done this work without the collaboration of industry partners, including Bandwidth," Reaves says. "And we are definitely interested in working with other companies in the telecom and tech sectors to help us move forward with efforts to address robocalls in a meaningful way."
The paper, "Diving into Robocall Content with SnorCall," will be presented Aug. 9 at the USENIX Security Symposium, which is being held in Anaheim, Calif. Lead author of the paper is Sathvik Prasad, a Ph.D. student at NC State. The paper was co-authored by Trevor Dunlap and Alexander Ross, both Ph.D. students at NC State.
The work was done with support from the National Science Foundation, under grants 1849994 and 2142930; the 2020 Facebook Internet Defense Prize; and the Google Cloud Research Credits program, under award GCP19980904.
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"Although telephone service providers, regulators and researchers have access to call metadata -- such as the number being called and the length of the call -- they do not have tools to investigate what is being said on robocalls at the vast scale required," says Brad Reaves, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.
"For one thing, providers don't want to listen in on calls -- it raises significant privacy concerns. But robocalls are a huge problem, and are often used to conduct criminal fraud. To better understand the scope of this problem, and gain insights into these scams, we need to know what is being said on these robocalls.
"We've developed a tool that allows us to the characterize the content of robocalls," Reaves says. "And we've done it without violating privacy concerns; in collaboration with a telecommunications company called Bandwidth, we operate more than 60,000 phone numbers that are used solely by us to monitor unsolicited robocalls. We did not use any phone numbers of actual customers."
The new tool, SnorCall, essentially records all robocalls received on the monitored phone lines. It bundles together robocalls that use the same audio, reducing the number of robocalls whose content needs to be analyzed by around an order of magnitude. These recorded robocalls are then transcribed and analyzed by a machine learning framework called Snorkel that can be used to characterize each call.
"SnorCall essentially uses labels to identify what each robocall is about," Reaves says. "Does it mention a specific company or government program? Does it request specific personal information? If so, what kind? Does it request money? If so, how much? This is all fed into a database that we can use to identify trends or behaviors."
As a proof of concept, the researchers used SnorCall to assess 232,723 robocalls collected over 23 months on the more than 60,000 phone lines dedicated to the study.
The latest weapon in the war on robocalls is an automated system that analyzes the content of these unsolicited bulk calls to shed light on both the scope of the problem and the type of scams being perpetuated by robocalls. The tool, called SnorCall, is designed to help regulators, phone carriers and other stakeholders better understand and monitor robocall trends -- and take action against related criminal activity.
"Although telephone service providers, regulators and researchers have access to call metadata -- such as the number being called and the length of the call -- they do not have tools to investigate what is being said on robocalls at the vast scale required," says Brad Reaves, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University.
"For one thing, providers don't want to listen in on calls -- it raises significant privacy concerns. But robocalls are a huge problem, and are often used to conduct criminal fraud. To better understand the scope of this problem, and gain insights into these scams, we need to know what is being said on these robocalls.
"We've developed a tool that allows us to the characterize the content of robocalls," Reaves says. "And we've done it without violating privacy concerns; in collaboration with a telecommunications company called Bandwidth, we operate more than 60,000 phone numbers that are used solely by us to monitor unsolicited robocalls. We did not use any phone numbers of actual customers."
The new tool, SnorCall, essentially records all robocalls received on the monitored phone lines. It bundles together robocalls that use the same audio, reducing the number of robocalls whose content needs to be analyzed by around an order of magnitude. These recorded robocalls are then transcribed and analyzed by a machine learning framework called Snorkel that can be used to characterize each call.
"SnorCall essentially uses labels to identify what each robocall is about," Reaves says. "Does it mention a specific company or government program? Does it request specific personal information? If so, what kind? Does it request money? If so, how much? This is all fed into a database that we can use to identify trends or behaviors."
As a proof of concept, the researchers used SnorCall to assess 232,723 robocalls collected over 23 months on the more than 60,000 phone lines dedicated to the study.
"For example, if investigators want to focus on a new scam topic, Snorkel is very good at identifying key terms or phrases associated with topics," Reaves says. "This could be a valuable feature for investigators who are focused on specific types of criminal fraud."
"Our findings demonstrate how illegal robocalls use major societal events like student loan forgiveness to develop new types of scams," says Sathvik Prasad, a Ph.D. student at NC State and first author of the paper. "SnorCall can aid stakeholders to monitor well-known robocall categories and also help them uncover new types of robocalls."
"There's no way we could have done this work without the collaboration of industry partners, including Bandwidth," Reaves says. "And we are definitely interested in working with other companies in the telecom and tech sectors to help us move forward with efforts to address robocalls in a meaningful way."
The paper, "Diving into Robocall Content with SnorCall," will be presented Aug. 9 at the USENIX Security Symposium, which is being held in Anaheim, Calif. Lead author of the paper is Sathvik Prasad, a Ph.D. student at NC State. The paper was co-authored by Trevor Dunlap and Alexander Ross, both Ph.D. students at NC State.
The work was done with support from the National Science Foundation, under grants 1849994 and 2142930; the 2020 Facebook Internet Defense Prize; and the Google Cloud Research Credits program, under award GCP19980904.
RELATED TOPICS
Computers & Math
Math Puzzles
Hacking
Encryption
Computers and Internet
Science & Society
Surveillance
Privacy Issues
Security and Defense
STEM Education
RELATED TERMS
Warfare
Phishing
Web crawler
Algebraic geometry
Information architecture
Search engine
Knot theory
Political corruption
Breaking
this hour
Fat Burning During Exercise Varies Widely
Muons and Secrets of the Universe
'Tattooing' Gold Nanopatterns Onto Live Cells
Microplastics Embedded in Tissues of Whales
A Climate-Orchestrated Early Human Love Story
Deep Freeze Ended Early Occupation of Europe
Gravity Sensing Mechanism in Plants
Bacteria Engineered to Detect Tumor DNA
Common Cold Virus and Blood Clotting Disorder
Oil Extraction Practice Triggers Tremors
Trending Topics
this week
SPACE & TIME
NASA
Mars
Nebulae
MATTER & ENERGY
Telecommunications
Detectors
Albert Einstein
COMPUTERS & MATH
Hacking
Encryption
Communications
advertisement
Strange & Offbeat
SPACE & TIME
Possible Seasonal Climate Patterns on Early Mars
Physicists Demonstrate How Sound Can Be Transmitted Through Vacuum
Chemical Contamination on International Space Station Is out of This World
MATTER & ENERGY
Muon G-2 Doubles Down With Latest Measurement, Explores Uncharted Territory in Search of New Physics
Tattoo Technique Transfers Gold Nanopatterns Onto Live Cells
Quantum Material Exhibits 'Non-Local' Behavior That Mimics Brain Function
COMPUTERS & MATH
The 'Unknome': A Database of Human Genes We Know Almost Nothing About
Self-Supervised AI Learns Physics to Reconstruct Microscopic Images from Holograms
Thermal Imaging Innovation Allows AI to See Through Pitch Darkness Like Broad Daylight