arXiv cs.CL· Ethan Traister, Dennis Tsang Ng, Siyu Zhang, Huaiyu Guo, Tommy Duong, Tyler Wu, Yuchen Zhou, Xingyu Shen, Jiaqi Wu, Simiao Ren·· 12 小时前AI 评分58
研究团队用主动语音智能体蜜罐采集 10,015 通真实诈骗与骚扰电话对话
A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot
AI 导读
研究团队发布由主动语音智能体蜜罐采集的真实诈骗与骚扰电话对话数据集，在 53 天内记录 10,015 通来电，其中 6,601 通含两轮以上对话，约 895 小时音频、328,869 条转写对话轮次，来自 5,665 个不同主叫号码。
正文 · 原文
Abstract:Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest: passive honeypots overwhelmingly capture automated messages and hang-ups, large-scale studies characterize call metadata rather than dialogue, and manual scam-baiting does not scale. We present a dataset of real scam-call conversations collected by an active voice-agent honeypot. Dedicated numbers are seeded into the lead-generation channels fraud operations harvest; inbound callers are answered by a low-latency conversational agent that adopts a plausible target persona and sustains the interaction while every call is recorded, transcribed, and automatically labeled. Over an initial 53-day window we captured 10,015 inbound scam and spam calls (6,601 with two or more turns): roughly 895 hours of audio and 328,869 transcribed turns from 5,665 distinct originating numbers. Under a holistic classifier the substantive calls are predominantly predatory-but-legal lead generation ("spam", about three in five), while about one in seven is an outright "scam" (949 in this snapshot). Each call carries a turn-level transcript, three-channel audio, per-turn latency telemetry, and layers of automatic labels, including a holistic scam/spam/legitimate judgment corroborated by independent human review (75% agreement on the binary decision). We describe the collection system, the record structure, and technical validation of the corpus's realism and label quality, including that the agent is recognized as non-human in only about 5% of engaged calls. We also benchmark established scam-detection methods, where detectors trained on published synthetic dialogue collapse in precision on real traffic.
| Comments: | 9 pages, 7 figures. Data descriptor. Companion analysis paper forthcoming |
| Subjects: | Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.29528 [cs.CR] |
| (or arXiv:2609.29528v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29528 arXiv-issued DOI via DataCite |
Submission history
From: Simiao Ren [view email]
[v1]
Tue, 25 Aug 2026 06:29:42 UTC (839 KB)
[v2]
Sat, 24 Oct 2026 21:26:32 UTC (841 KB)
来源：arXiv cs.CL · arxiv.org