Who's Calling? Characterizing Robocalls through Audio and Metadata Analysis
Who's Calling? Characterizing Robocalls through Audio and Metadata Analysis
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发表时间：
2020
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通讯作者：
S. Prasad;Elijah Bouma-Sims;Athishay Kiran Mylappan;Bradley Reaves
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作者：
S. Prasad;Elijah Bouma-Sims;Athishay Kiran Mylappan;Bradley Reaves
未经请求的电话是当今个人面临的最突出的安全问题之一。尽管对这个问题进行了广泛的轶事式讨论，但许多重要问题仍未得到解答。在本文中，我们首次对11个月内多达66,606个电话的蜜罐进行了大规模的纵向分析。从呼叫元数据中，我们描述了主动呼叫的长期趋势，开发了第一种测量语音邮件垃圾邮件、wangiri攻击的技术，并确定了无法解释的高容量呼叫事件。此外，我们机械地回答我们收到的呼叫尝试的子集，将相关呼叫集群到操作活动中，使我们能够描述这些活动如何使用电话号码。关键是，我们没有发现任何证据表明接听主动电话会增加接到的主动电话数量，这推翻了普遍的看法。我们还发现，我们可以可靠地隔离个人电话活动，在此过程中揭示了两种不同的社会安全骗局的程度，同时经验表明，大多数活动很少重复使用电话号码。这些分析为研究人员、调查人员和陷入困境的公众提供了强大的新工具和视角。
Unsolicited calls are one of the most prominent security issues facing individuals today. Despite wide-spread anec-dotal 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.