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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

  • Paper accepted to ICME 2025 June 24, 2025
  • SPIES Lab’s Browser Fingerprinting Work in the News June 23, 2025
  • Journal paper accepted to IEEE TIFS June 19, 2025
  • SPIES Lab’s Browser Fingerprinting Work Features in News June 18, 2025
  • Paper Accepted to USENIX Security 2025 June 6, 2025
  • 2 Papers Accepted to PST 2025 June 6, 2025
  • AI Spies News — BPSniff (IEEE S&P 2025) Paper News Story May 12, 2025
  • Launching the AI Spies News Channel May 12, 2025
  • Paper accepted to WiSec 2025 May 11, 2025
  • SPIES Lab’s Secure Messaging Work Features in News May 3, 2025
  • SPIES Lab Student to Start as an Assistant Professor April 18, 2025
  • Dr. Saxena’s Primer on Secure Communications in News Media March 31, 2025
  • Dr. Saxena recognized with the Dean’s Excellence Award! February 14, 2025
  • Dr. Saxena appointed as the Senior Area Editor, IEEE TIFS February 6, 2025
  • 2 Full Papers Accepted to WWW 2025 January 20, 2025
  • Journal paper accepted to IEEE TMC December 18, 2024
  • New post-doctoral researcher joins the lab December 11, 2024
  • Paper Accepted to ACM Computing Surveys 2024 November 30, 2024
  • Paper Accepted to IEEE S&P 2025 October 21, 2024
  • Paper Accepted to Nature Human Behaviour October 20, 2024

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