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   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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2. 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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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. How Non-Experts Try to Detect Phishing Scam Emails

   Nthala, Norbert; Wash, Rick (May 2021, Workshop on Consumer Protection)

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   Email remains one of the most widely used methods of communication globally. However, successful phishing email attacks and subsequent costs remain unreasonably high despite technical advances in defenses that limit phishing scams. In this paper, we examine human detection of phishing. We found that non-experts go through four different sensemaking processes to determine if an email is a phishing message; they use different knowledge and skills to become suspicious differently in each process. Additionally, non-experts rely on their social connections as an investigative tool to determine if an email is a phishing scam. We discuss the impact of our findings on phishing training and technology. 

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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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