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Neural variability fingerprint predicts individuals' information security violation intentions.

Qin Duan1, Zhengchuan Xu2, Qing Hu3

  • 1Department of Psychology, Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-Sen University, Guangzhou 510006, China.

Fundamental Research
|June 27, 2024
PubMed
Summary

Neural variability patterns can predict individuals' intentions to violate information security policies. This study combined functional magnetic resonance imaging (fMRI) with a machine learning model to identify these predictive neural markers.

Keywords:
Information securityInformation security violationMachine learningNeural variabilityfMRI

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Area of Science:

  • Cognitive Neuroscience
  • Information Security
  • Neuroimaging

Background:

  • Individuals are the weakest link in organizational information security.
  • Understanding individual information security behaviors is crucial.
  • Traditional methods for studying security behaviors may have biases.

Purpose of the Study:

  • To investigate if neural variability patterns predict information security violation intentions.
  • To explore the utility of combining functional magnetic resonance imaging (fMRI) with a machine learning approach.
  • To offer a novel perspective on information security behavior using neuroscientific methods.

Main Methods:

  • Utilized an adapted Information Security Paradigm (ISP) task.
  • Employed functional magnetic resonance imaging (fMRI) during task performance.
  • Developed a predictive model using machine learning on neural variability data.

Main Results:

  • People are more likely to act under neutral conditions compared to violation contexts.
  • A neural variability predictive model was successfully built.
  • The model, incorporating specific brain networks, accurately predicted violation intentions.

Conclusions:

  • Neural variability is a valuable predictor of information security violation intentions.
  • The integration of ISP and fMRI offers a new avenue for exploring neural predictive models.
  • This approach provides insights into psychological processes underlying security behaviors without common biases.