Towards Measuring the Effectiveness of Telephony Blacklists
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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 talk, Pandit will show the systematic investigation of 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, four different data sources are considered: 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, and honeypot-based phone call audio recordings. Overall, the 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. Pandit proposes 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.