Fan, Z.; Li, W.; Laskey, K.B.; Chang, K.-C. Investigation of Phishing Susceptibility with Explainable Artificial Intelligence. Future Internet2024, 16, 31.
Fan, Z.; Li, W.; Laskey, K.B.; Chang, K.-C. Investigation of Phishing Susceptibility with Explainable Artificial Intelligence. Future Internet 2024, 16, 31.
Fan, Z.; Li, W.; Laskey, K.B.; Chang, K.-C. Investigation of Phishing Susceptibility with Explainable Artificial Intelligence. Future Internet2024, 16, 31.
Fan, Z.; Li, W.; Laskey, K.B.; Chang, K.-C. Investigation of Phishing Susceptibility with Explainable Artificial Intelligence. Future Internet 2024, 16, 31.
Abstract
As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. 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. 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.