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.
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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 = {https://www.usenix.org/conference/usenixsecurity23/presentation/pandit},
publisher = {USENIX Association},
month = aug
}