A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta
{"title":"Uncovering the Landscape of Fraud and Spam in the Telephony Channel","authors":"A. Marzuoli, H. Kingravi, David Dewey, Robert S. Pienta","doi":"10.1109/ICMLA.2016.0153","DOIUrl":null,"url":null,"abstract":"Robocalling, voice phishing, and caller ID spoofing are common cybercrime techniques used to launch scam campaigns through the telephony channel, which unsuspecting users have long trusted. More reliable than online complaints, a telephony honeypot provides complete, accurate and timely information about unwanted phone calls across the United States. Our first goal is to provide a large-scale data-driven analysis of the telephony spam and fraud ecosystem. Our second goal is to uniquely identify bad actors potentially operating several phone numbers. We collected about 40,000 unsolicited calls. Our results show that only a few bad actors, robocallers or telemarketers, are responsible for the majority of the spam and scam calls, and that they can be uniquely identified based on audio features from their calls. This discovery has major implications for law enforcement and businesses that are presently engaged in combatting the rise of telephony fraud. In particular, since our system allows endusers to detect fraudulent behavior and tie it back to existing fraud and spam campaigns, it can be used as the first step towards designing and deploying intelligent defense strategies.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2016.0153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Robocalling, voice phishing, and caller ID spoofing are common cybercrime techniques used to launch scam campaigns through the telephony channel, which unsuspecting users have long trusted. More reliable than online complaints, a telephony honeypot provides complete, accurate and timely information about unwanted phone calls across the United States. Our first goal is to provide a large-scale data-driven analysis of the telephony spam and fraud ecosystem. Our second goal is to uniquely identify bad actors potentially operating several phone numbers. We collected about 40,000 unsolicited calls. Our results show that only a few bad actors, robocallers or telemarketers, are responsible for the majority of the spam and scam calls, and that they can be uniquely identified based on audio features from their calls. This discovery has major implications for law enforcement and businesses that are presently engaged in combatting the rise of telephony fraud. In particular, since our system allows endusers to detect fraudulent behavior and tie it back to existing fraud and spam campaigns, it can be used as the first step towards designing and deploying intelligent defense strategies.