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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 BHI 2026 September 26, 2026
  • SPIES Lab Student Named Future Faculty Fellow (F3) September 18, 2026
  • 2 Papers Accepted to ISC 2026 September 12, 2026
  • Dr. Saxena appointed as the Raytheon Professor! August 19, 2026
  • Two SPIES graduates joining industry and academia July 28, 2026
  • Dr. Saxena to Co-Lead a New $2M NSF SFS (Cyber AI) Program July 27, 2026
  • Introducing Dr. Huq, SPIES Lab’s 19th PhD Graduate June 8, 2026
  • Introducing Dr. Dani, SPIES Lab’s 18th PhD Graduate May 24, 2026
  • SPIES Lab Student Wins the Future Faculty Fellowship May 21, 2026
  • SPIES Lab PhD Student Received NSF Fellowship to Make AI More Secure April 24, 2026
  • SPIES Lab’s VitalID Research Featured in National Media Coverage April 18, 2026
  • Dr. Saxena drives a new MS Cybersecurity degree at Texas A&M (launching Fall 2026) March 28, 2026
  • SPIES Lab PhD Student Wins Graduate Research Excellence Award 2026 March 6, 2026
  • Paper accepted to IEEE Internet Computing February 22, 2026
  • Paper accepted to WiSec 2026 January 13, 2026
  • Paper accepted to EACL 2026 January 5, 2026
  • Paper accepted to USENIX Security 2026 December 17, 2025
  • SPIES Lab Research Cited in News Coverage on AI Security Breach December 14, 2025
  • SPIES Lab Research Featured in TAMU College of Engineering Spotlight December 5, 2025
  • Paper accepted to IJCNLP-AACL 2025 October 25, 2025

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