PhD Proposal by Sharbani Pandit

Event Details
  • Date/Time:
    • Wednesday October 7, 2020 - Thursday October 8, 2020
      10:00 am - 11:59 am
  • Phone:
  • URL: BlueJeans Link
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Summary Sentence: Combating Robocalls to Enhance Trust in Converged Telephony

Full Summary: No summary paragraph submitted.

Title: Combating Robocalls to Enhance Trust in Converged Telephony


Sharbani Pandit

School of Computer Science

Georgia Institute of Technology



Date: Wednesday, October 7th, 2020

Time: 10:00 AM -12:00 PM (EDT)


**Note: this proposal is remote-only**



Dr. Mustaque Ahamad (Advisor), School of Computer Science, Georgia Institute of Technology

Dr. Roberto Perdisci, School of Computer Science, University of Georgia, Georgia Institute of Technology

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



Telephone scams are now on the rise and without effective countermeasures the number of scam/spam call people receive will continue to increase. Voice scams have become such a serious problem that people often no longer pick up calls from unknown callers. 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 offering a novel interaction model that can help enhance the trust and effectiveness of voice interactions. 


We study how to automatically build phone blacklists from multiple data sources and evaluate the effectiveness of such blacklists in the stopping current robocalls. To address the threat model where caller ID is spoofed, we introduce the notion of a virtual assistant aimed at filtering out unwanted calls. To this end, we developed a Smartphone based app named RobocallGuard which can pick up calls from unknown callers on behalf of the callee and filter out non-targeted calls. We conduct a user study that shows that users are comfortable with a virtual assistant filtering out unwanted calls on their behalf. Finally, we introduce SmartVA which can effectively block targeted robocalls. SmartVA 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. By making these contributions, we aim at bringing trust back to the telephony channel and making a better telephony experience for everyone. 

Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Sep 29, 2020 - 2:30pm
  • Last Updated: Sep 29, 2020 - 2:30pm