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SPIES Lab, Computer Science and Engineering

Texas A&M University College of Engineering

fNIRS

Study of Website Legitimacy and Familiarity

In this paper, we study the neural underpinnings relevant to user-centered web security through the lens of functional near-infrared spectroscopy (fNIRS). Specifically, we design and conduct a fNIRS study to pursue a thorough investigation of users’ processing of legitimate vs. illegitimate and familiar vs. unfamiliar websites. We pinpoint the neural activity in these tasks as well as the brain areas that control such activity. We show that, at the neurological level, users process the legitimate websites differently from the illegitimate websites when subject to phishing attacks. Similarly, we show that users exhibit marked differences in the way their brains process the previously familiar websites from unfamiliar websites. These findings have several defensive and offensive implications. In particular, we discuss how these differences may be used by the system designers in the future to differentiate between legitimate and illegitimate websites automatically based on neural signals. Similarly, we discuss the potential for future malicious attackers, with access to neural signals, in compromising the privacy of users by detecting whether a website is previously familiar or un-familiar to the user.
Compared to prior research, our novelty lies in several aspects. First, we employ a neuroimaging methodology (fNIRS) not tapped into by prior security research for the problem domain we are studying. Second, we provide a focused study design and comprehensive investigation of the neural processing underlying the specific tasks of legitimate vs. illegitimate and familiar vs. unfamiliar websites. Third, we use an experimental set-up much more amenable to real-world settings, compared to previous fMRI studies. Beyond these scientific innovations, our work also serves to corroborate and extend several of the findings of the prior literature with independent methodologies, tools and settings.

Experimental Setting

Experimental Setting

People

Faculty

  • Nitesh Saxena

Student

  • Ajaya Neupane (@UAB; PhD 2017)

External Collaborators:

  • Leanne Hirshfield (Syracuse University)

Publication

  • Neural Underpinnings of Website Legitimacy and Familiarity Detection: An fNIRS Study.
    Ajaya Neupane, Nitesh Saxena and Leanne Hirshfield.
    In Security and Privacy Track, the World-Wide Web Conference (WWW), 2017.
    [pdf]

Recent News

  • “Neuro Security” work got a MURI award from AFOSR March 22, 2023
  • Paper accepted to Oakland 2023 March 14, 2023
  • Paper (conditionally) accepted to MobiSys 2023 February 27, 2023
  • Paper accepted to USENIX Security 2023 February 21, 2023
  • 2 full papers accepted to WiSec 2023 January 30, 2023
  • Cybersecurity Program Led By Dr. Saxena Ranks Best! January 26, 2023
  • EarSpy in Media January 26, 2023
  • Dr. Saxena is a Co-PI on Thematic AI Lab November 28, 2022
  • Paper accepted to PMC 2022 November 28, 2022
  • Paper accepted to ICISC 2022 November 28, 2022
  • A New Grant from NSA October 17, 2022
  • Dr. Saxena appointed as a Dean’s Research Fellow October 17, 2022
  • Dr. Saxena to lead a new SaTC Medium project on Election Security July 16, 2022
  • SPIES Lab’s 12th PhD Graduate — Anuradha Mandal July 16, 2022
  • SPIES Lab’s 11th PhD Graduate – Payton Walker July 6, 2022
  • Two papers accepted to PST 2022 June 9, 2022
  • Paper accepted to ICDCS 2022 April 4, 2022
  • Paper accepted CHIL 2022 March 19, 2022
  • 2 papers accepted to WiSec 2022 March 19, 2022
  • Paper accepted to EuroS&P 2022 February 12, 2022

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