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Title: Combating Robocalls to Enhance Trust in Converged Telephony
Sharbani Pandit
Ph.D. Candidate
School of Computer Science
College of Computing
Date: Thursday, August 19th, 2021
Time: 12:00pm - 2:00pm
Location: https://bluejeans.com/592327666/9832
Committee:
Dr. Mustaque Ahamad (Advisor, School of Computer Science, Georgia Institute of Technology)
Dr. Roberto Perdisci (School of Computer Science, Georgia Institute of Technology, University of Georgia)
Dr. Diyi Yang (School of Interactive Computing, Georgia Institute of Technology)
Dr. Mostafa Ammar (School of Computer Science, Georgia Institute of Technology)
Dr. Lillian Lo (Principal Data Scientist, AT&T)
Abstract:
Telephone scams are now on the rise and without effective countermeasures there is no stopping. The number of scam/spam calls people receive is increasing every day. YouMail estimates that June 2021 saw 4.4 billion robocalls in the United States and the Federal Trade Commission (FTC) phone complaint portal receives millions of complaints about such fraudulent and unwanted calls each year. Voice scams have become such a serious problem that people often no longer pick up calls from unknown callers. In several scams that have been reported widely, the telephony channel is either directly used to reach potential victims or as a way to monetize scams that are advertised online, as in the case of tech support scams. The vision of this research is to bring trust back to the telephony channel. We believe this can be done by stopping unwanted and fraud calls and leveraging smartphones to offer a novel interaction model that can help enhance the trust in voice interactions. Thus, our research explores defenses against unwanted calls that include blacklisting of known callers, detecting robocalls in presence of caller ID spoofing and proposing a novel virtual assistant that can stop more sophisticated robocalls without user intervention.
We first explore phone blacklists to stop unwanted calls based on the caller ID received when a call arrives. We study how to automatically build blacklists from multiple data sources and evaluate the effectiveness of such blacklists in stopping current robocalls. We also used insights gained from this process to increase detection of more sophisticated robocalls and improve the robustness of our defense system against malicious callers who can use techniques like caller ID spoofing. To address the threat model where caller ID is spoofed, we introduce the notion of a virtual assistant. To this end, we developed a Smartphone based app name RobocallGuard which can pick up calls from unknown callers on behalf of the user and detect and filter out unwanted calls. We conduct a user study that shows that users are comfortable with a virtual assistant stopping unwanted calls on their behalf. Moreover, most users reported that such a virtual assistant is beneficial to them. Finally, we expand our threat model and introduce RobocallGuardPlus which can effectively block targeted robocalls. RobocallGuardPlus also picks up calls from unknown callers on behalf of the callee and engages in a natural conversation with the caller. RobocallGuardPlus uses a combination of NLP based machine learning models to determine if the caller is a human or a robocaller. To the best of our knowledge, we are the first to develop such a defense system that can interact with the caller and detect robocalls where robocallers utilize caller ID spoofing and voice activity detection to bypass the defense mechanism. Security analysis explored by us shows that such a system is capable of stopping more sophisticated robocallers that might emerge in the near future. By making these contributions, we believe we can bring trust back to the telephony channel and provide a better call experience for everyone.
