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Predicting User Susceptibility to Phishing Based on Multidimensional Features.

Rundong Yang1, Kangfeng Zheng1, Bin Wu1

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This study predicts user susceptibility to phishing emails using a multidimensional model. Machine learning accurately identified potential victims, highlighting the human element in network security threats.

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Area of Science:

  • Cybersecurity
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Phishing remains a significant network security threat, exploiting human vulnerabilities.
  • Existing antiphishing techniques require enhancement to address the human element effectively.

Purpose of the Study:

  • To develop and validate a multidimensional phishing susceptibility prediction model (MPSPM).
  • To identify key features predicting an individual's susceptibility to phishing attacks.

Main Methods:

  • Collected data from 1105 volunteers via questionnaires on demographics, personality, knowledge, security behavior, and cognitive processes.
  • Constructed legitimate and phishing email datasets for experimental simulation.
  • Applied 7 supervised machine learning algorithms to classify users as susceptible or nonsusceptible.

Main Results:

  • Machine learning models achieved high accuracy in predicting user phishing susceptibility.
  • The maximum prediction accuracy rate reached 89.04% using the proposed multidimensional approach.

Conclusions:

  • The multidimensional phishing susceptibility prediction model demonstrates significant potential for identifying at-risk individuals.
  • Findings underscore the importance of understanding human factors in cybersecurity and developing targeted interventions.