Combating Robocalls with Phone Virtual Assistant Mediated Interaction


Sharbani Pandit, Georgia Institute of Technology; Krishanu Sarker, Georgia State University; Roberto Perdisci, University of Georgia and Georgia Institute of Technology; Mustaque Ahamad and Diyi Yang, Georgia Institute of Technology


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 detection and blocking of spoofed robocalls, we propose a NLP based smartphone virtual assistant that automatically vets incoming calls. Similar to a human assistant, the virtual assistant picks up an incoming call and uses machine learning models to interact with the caller to determine if the call source is a human or a robocaller. It interrupts a user by ringing the phone only when the call is determined to be not from a robocaller. Security analysis performed by us shows that such a system can stop current and more sophisticated robocallers that might emerge in the future. We also conduct a user study that shows that the virtual assistant can preserve phone call user experience.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {285465,
author = {Sharbani Pandit and Krishanu Sarker and Roberto Perdisci and Mustaque Ahamad and Diyi Yang},
title = {Combating Robocalls with Phone Virtual Assistant Mediated Interaction},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {463--479},
url = {},
publisher = {USENIX Association},
month = aug

Presentation Video