Investigation of Phishing Susceptibility with Explainable Artificial Intelligence

Zhengyang Fan (), Wanru Li, Kathryn Blackmond Laskey and Kuo-Chu Chang
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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

Future Internet, 2024, vol. 16, issue 1, 1-18

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.

Keywords: phishing susceptibility; cyber security; interpretable artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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