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

   Prasad, Sathvik; Bouma-Sims, Elijah; Kiran Mylappan, Athishay; Reaves, Bradley (August 2020, Proceedings of the 2020 USENIX Security Symposium)

   null (Ed.)

   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 large-scale, 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. 

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2. UCBlocker: Unwanted Call Blocking Using Anonymous Authentication

   Du, Changlai; Yu, Hexuan; Xiao, Yang; Hou, Y. Thomas; Keromytis, Angelos D.; Lou, Wenjing (August 2023, 32nd USENIX Security Symposium (USENIX Security 23))

   Telephone users are receiving more and more unwanted calls including spam and scam calls because of the transfer-without-verification nature of global telephone networks, which allows anyone to call any other numbers. To avoid unwanted calls, telephone users often ignore or block all incoming calls from unknown numbers, resulting in the missing of legitimate calls from new callers. This paper takes an end-to-end perspective to present a solution to block unwanted calls while allowing users to define the policies of acceptable calls. The proposed solution involves a new infrastructure based on anonymous credentials, which enables anonymous caller authentication and policy definition. Our design decouples caller authentication and call session initiation and introduces a verification code to interface and bind the two processes. This design minimizes changes to telephone networks, reduces latency to call initiation, and eliminates the need for a call-time data channel. A prototype of the system is implemented to evaluate its feasibility. 

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3. Diving into Robocall Content with SnorCall

   Prasad, Sathvik; Dunlap, Trevor; Ross, Alexander; Reaves, Bradley (August 2023, 2023 Usenix Security Symposium)

   Unsolicited bulk telephone calls — termed "robocalls" — nearly outnumber legitimate calls, overwhelming telephone users. While the vast majority of these calls are illegal, they are also ephemeral. Although telephone service providers, regulators, and researchers have ready access to call metadata, they do not have tools to investigate call content at the vast scale required. This paper presents SnorCall, a framework that scalably and efficiently extracts content from robocalls. SnorCall leverages the Snorkel framework that allows a domain expert to write simple labeling functions to classify text with high accuracy. We apply SnorCall to a corpus of transcripts covering 232,723 robocalls collected over a 23-month period. Among many other findings, SnorCall enables us to obtain first estimates on how prevalent different scam and legitimate robocall topics are, determine which organizations are referenced in these calls, estimate the average amounts solicited in scam calls, identify shared infrastructure between campaigns, and monitor the rise and fall of election-related political calls. As a result, we demonstrate how regulators, carriers, anti-robocall product vendors, and researchers can use SnorCall to obtain powerful and accurate analyses of robocall content and trends that can lead to better defenses. 

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4. The Effects of Nonresponse and Sampling Omissions on Estimates on Various Topics in Federal Surveys: Telephone and IVR Surveys of Address-Based Samples

   https://doi.org/10.2478/jos-2020-0032  

   Fowler, Floyd J.; Brenner, Philip; Roman, Anthony M.; Hargraves, J. Lee (July 2020, Journal of Official Statistics)

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   Abstract With declining response rates and challenges of using RDD sampling for telephone surveys, collecting data from address-based samples has become more attractive. Two approaches are doing telephone interviews at telephone numbers matched to addresses and asking those at sampled addresses to call into an Interactive Voice Response (IVR) system to answer questions. This study used in-person interviewing to evaluate the effects of nonresponse and problems matching telephone numbers when telephone and IVR were used as the initial modes of data collection. The survey questions were selected from major US federal surveys covering a variety of topics. Both nonresponse and, for telephone, inability to find matches result in important nonresponse error for nearly half the measures across all topics, even after adjustments to fit the known demographic characteristics of the residents. Producing credible estimates requires using supplemental data collection strategies to reduce error from nonresponse. 

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5. Towards a Practical Differentially Private Collaborative Phone Blacklisting System

   https://doi.org/10.1145/3427228.3427239  

   Ucci, Daniele; Perdisci, Roberto; Lee, Jaewoo; Ahamad, Mustaque (December 2020, ACSAC 2020: Annual Computer Security Applications Conference)

   null (Ed.)

   Spam phone calls have been rapidly growing from nuisance to an increasingly effective scam delivery tool. To counter this increasingly successful attack vector, a number of commercial smartphone apps that promise to block spam phone calls have appeared on app stores, and are now used by hundreds of thousands or even millions of users. However, following a business model similar to some online social network services, these apps often collect call records or other potentially sensitive information from users’ phones with little or no formal privacy guarantees. In this paper, we study whether it is possible to build a practical collaborative phone blacklisting system that makes use of local differential privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the challenges and trade-offs related to using LDP, evaluate our LDP-based system on real-world user-reported call records collected by the FTC, and show that it is possible to learn a phone blacklist using a reasonable overall privacy budget and at the same time preserve users’ privacy while maintaining utility for the learned blacklist. 

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