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Investigation of Phishing Susceptibility with Explainable Artificial Intelligence

Author

Listed:
  • Zhengyang Fan

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

  • Wanru Li

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

  • Kathryn Blackmond Laskey

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

  • Kuo-Chu Chang

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA)

Abstract

Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

Suggested Citation

  • Zhengyang Fan & Wanru Li & Kathryn Blackmond Laskey & Kuo-Chu Chang, 2024. "Investigation of Phishing Susceptibility with Explainable Artificial Intelligence," Future Internet, MDPI, vol. 16(1), pages 1-18, January.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:1:p:31-:d:1320799
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    References listed on IDEAS

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    1. Matthew Canham & Clay Posey & Delainey Strickland & Michael Constantino, 2021. "Phishing for Long Tails: Examining Organizational Repeat Clickers and Protective Stewards," SAGE Open, , vol. 11(1), pages 21582440219, January.
    2. Andronicus A. Akinyelu & Aderemi O. Adewumi, 2014. "Classification of Phishing Email Using Random Forest Machine Learning Technique," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-6, April.
    3. Michael Workman, 2008. "Wisecrackers: A theory‐grounded investigation of phishing and pretext social engineering threats to information security," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(4), pages 662-674, February.
    4. Padmalochan Panda & Alekha Kumar Mishra & Deepak Puthal, 2022. "A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems," Future Internet, MDPI, vol. 14(8), pages 1-17, August.
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