Vafa's research focused on leveraging machine learning for end-user security and privacy protection. This was achieved by strengthening the security and privacy defenses over the network stack: in application layer I worked on applying machine learning to build a human-centered anti-fingerprinting defense. In mid-layers I work on the MUD standard for protecting IoT devices through network-microsegmentation., and close to physical layer, I worked on deep learning applications in binary analysis for facilitating reverse engineering of stripped IoT firmware binaries.


  • MUD
  • BrowserFingerprintingCountermeasures
  • FacilitatingReverseEngineeringoftheIoTfirmware

Recent Publications

  • Jacob Abbott, Jayati Dev, Donginn Kim, Shakthidhar Gopavaram, Meera Iyer, Shivani Sadam, Shrirang Mare, Tatiana Ringenberg, Vafa Andalibi, and L. Jean Camp. Privacy Lessons Learnt from Deploying an IoT Ecosystem in the Home. In Proceedings of the 2022 European Symposium on Usable Security, EuroUSEC '22, New York, NY, USA, pages 98–110, 2022. Association for Computing Machinery.
    Keywords: Security, user interviews, smart home, IoT, 2FA, privacy. [bibtex-entry]

  • Vafa Andalibi, Jayati Dev, DongInn Kim, Eliot Lear, and L Jean Camp. Is Visualization Enough? Evaluating the Efficacy of MUD-Visualizer in Enabling Ease of Deployment for Manufacturer Usage Description (MUD). In Annual Computer Security Applications Conference, pages 337--348, December 2021.
    Keywords: IoT, MUD, MUD-Visualizer. [bibtex-entry]

  • Vafa Andalibi, Jayati Dev, DongInn Kim, Eliot Lear, and Jean Camp. Making Access Control Easy in IoT. In IFIP International Symposium on Human Aspects of Information Security & Assurance, June 2021.
    Keywords: IoT, MUD, MUD-Visualizer. [bibtex-entry]

  • Personal Website / CV

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