The convergence of telephony with the Internet has led to numerous new attacks that make use of phone calls to defraud victims. In response to the increasing number of unwanted or fraudulent phone calls, a number of call blocking applications have appeared on smartphone app stores, including a recent update to the default Android phone app that alerts users of suspected spam calls. However, little is known about the methods used by these apps to identify malicious numbers, and how effective these methods are in practice. In this paper, we are the first to systematically investigate multiple data sources that may be leveraged to automatically learn phone blacklists, and to explore the potential effectiveness of such blacklists by measuring their ability to block future unwanted phone calls. Specifically, we consider four different data sources: user-reported call complaints submitted to the Federal Trade Commission (FTC), complaints collected via crowd-sourced efforts (e.g., 800notes.com), call detail records (CDR) from a large telephony honeypot [1], and honeypot-based phone call audio recordings. Overall, our results show that phone blacklists are capable of blocking a significant fraction of future unwanted calls (e.g., more than 55%). Also, they have a very low false positive rate of only 0.01% for phone numbers of legitimate businesses. We also propose an unsupervised learning method to identify prevalent spam campaigns from different data sources, and show how effective blacklists may be as a defense against such campaigns.

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Effective and reliable screening of patients via Computer-Aided Diagnosis can play a crucial part in the battle against COVID-19. Most of the existing works focus on developing sophisticated methods yielding high detection performance, yet not addressing the issue of predictive uncertainty. In this work, we introduce uncertainty estimation to detect confusing cases for expert referral to address the unreliability of stateof-the-art (SOTA) DNNs on COVID-19 detection. To the best of our knowledge, we are the first to address this issue on the COVID-19 detection problem. In this work, we investigate a number of SOTA uncertainty estimation methods on publicly available COVID dataset and present our experimental findings. In collaboration with medical professionals, we further validate the results to ensure the viability of the best performing method in clinical practice.

Mass robocalls affect millions of people on a daily basis. Unfortunately, most current defenses against robocalls rely on phone blocklists and are ineffective against caller ID spoofing. To enable the detection of spoofed robocalls, we propose a virtual assistant application that could be integrated on smartphones to automatically vet incoming calls. Similar to a human assistant, the virtual assistant can pick up an incoming call and screen it without user interruption to determine if the call is unwanted. Via a user study, we show that our virtual assistant is able to preserve the user experience of a typical phone call. At the same time, we show that our system can detect mass robocalls without negatively impacting legitimate callers.