Difference between revisions of "Sahin2017"

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|Month=July
 
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|Booktitle=Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017)
 
|Booktitle=Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017)
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|URL=http://s3.eurecom.fr/docs/soups17_sahin.pdf
 
|Series=SOUPS'17
 
|Series=SOUPS'17
 
|Abstract=A new countermeasure recently appeared to fight back against unwanted phone calls (such as, telemarketing, survey or scam calls), which consists in connecting back the telemarketer with a phone bot (“robocallee”) which mimics a real persona. Lenny is such a bot (a computer program) which plays a set of pre-recorded voice messages to interact with the spammers. Although not based on any sophisticated artificial intelligence, Lenny is surprisingly effective in keeping the conversation going for tens of minutes. Moreover, it is clearly recognized as a bot in only 5% of the calls recorded in our dataset. In this paper, we try to understand why Lenny is so successful in dealing with spam calls. To this end, we analyze the recorded conversations of Lenny with various types of spammers. Among 487 publicly available call recordings, we select 200 calls and transcribe them using a commercial service. With this dataset, we first explore the spam ecosystem captured by this chatbot, presenting several statistics on Lenny’s interaction with spammers. Then, we use conversation analysis to understand how Lenny is adjusted with the sequential context of such spam calls, keeping a natural flow of conversation. Finally, we discuss a range of research and design issues to gain a better understanding of chatbot conversations and to improve their efficiency.
 
|Abstract=A new countermeasure recently appeared to fight back against unwanted phone calls (such as, telemarketing, survey or scam calls), which consists in connecting back the telemarketer with a phone bot (“robocallee”) which mimics a real persona. Lenny is such a bot (a computer program) which plays a set of pre-recorded voice messages to interact with the spammers. Although not based on any sophisticated artificial intelligence, Lenny is surprisingly effective in keeping the conversation going for tens of minutes. Moreover, it is clearly recognized as a bot in only 5% of the calls recorded in our dataset. In this paper, we try to understand why Lenny is so successful in dealing with spam calls. To this end, we analyze the recorded conversations of Lenny with various types of spammers. Among 487 publicly available call recordings, we select 200 calls and transcribe them using a commercial service. With this dataset, we first explore the spam ecosystem captured by this chatbot, presenting several statistics on Lenny’s interaction with spammers. Then, we use conversation analysis to understand how Lenny is adjusted with the sequential context of such spam calls, keeping a natural flow of conversation. Finally, we discuss a range of research and design issues to gain a better understanding of chatbot conversations and to improve their efficiency.
 
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Revision as of 06:43, 5 July 2019

Sahin2017
BibType INPROCEEDINGS
Key Sahin2017
Author(s) Merve Sahin, Marc Relieu, Aurélien Francillon
Title Using chatbots against voice spam: Analyzing Lenny’s effectiveness
Editor(s)
Tag(s) EMCA, Telephony, Fraud, AI, Chatbots, Conversational AI
Publisher USENIX
Year 2017
Language
City Santa Clara
Month July
Journal
Volume
Number
Pages
URL Link
DOI
ISBN
Organization
Institution
School
Type
Edition
Series SOUPS'17
Howpublished
Book title Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017)
Chapter

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Abstract

A new countermeasure recently appeared to fight back against unwanted phone calls (such as, telemarketing, survey or scam calls), which consists in connecting back the telemarketer with a phone bot (“robocallee”) which mimics a real persona. Lenny is such a bot (a computer program) which plays a set of pre-recorded voice messages to interact with the spammers. Although not based on any sophisticated artificial intelligence, Lenny is surprisingly effective in keeping the conversation going for tens of minutes. Moreover, it is clearly recognized as a bot in only 5% of the calls recorded in our dataset. In this paper, we try to understand why Lenny is so successful in dealing with spam calls. To this end, we analyze the recorded conversations of Lenny with various types of spammers. Among 487 publicly available call recordings, we select 200 calls and transcribe them using a commercial service. With this dataset, we first explore the spam ecosystem captured by this chatbot, presenting several statistics on Lenny’s interaction with spammers. Then, we use conversation analysis to understand how Lenny is adjusted with the sequential context of such spam calls, keeping a natural flow of conversation. Finally, we discuss a range of research and design issues to gain a better understanding of chatbot conversations and to improve their efficiency.

Notes