# Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
## 1 Introduction  
A phishing attack is a form of identity theft wherein a malicious website mimics a genuine one, to illicitly obtain sensitive information like passwords, account details, or credit card numbers [1]. These exploits have caused significant losses to both corporations and government organizations. For example, the infamous cyber/phishing attack against the US Government Office of Personnel Management resulted in attackers gaining access to sensitive data on millions of government employees and contractors. In 2018, there was a 40% increase in phishing attacks targeted at US organizations [2]. According to the FBI Internet Crime Complaint Center, there were more than 2018 complaints resulting in losses of over 1.2 billion due to business email compromises. Based on the most recent FBI Internet Crime Annual Report, the incidence of phishing attacks has surged to its highest level since 2019, resulting in a significantly larger number of victims compared to personal data breaches, which ranked second in terms of victim count in 2022. Additionally, the financial losses associated with internet crimes, including phishing, reached a staggering 10.3 billion in 2022, nearly doubling the financial impact observed in 2021. Conducting research to understand the factors contributing to an individual's susceptibility to phishing attacks is crucial in enhancing cybersecurity awareness and developing effective protective measures. However, some existing literature in this area has mainly focused on building models to achieve accurate predictions for phishing behavior. Althoughthese models may improve performance, their interpretability can be challenging, making it difficult to guide researchers in developing targeted educational and awareness campaigns to prevent and mitigate the impact of phishing attacks [3; 4]. Other works have relied on statistical tests to identify factors related to phishing susceptibility, but these approaches may not provide clear guidance on how the identified factors influence susceptibility [5; 6; 7; 8]. To address these issues, this paper proposes a deep neural network (DNN) approach powered by a local explainable artificial intelligence (XAI) technique called SHAP [9]. The proposed framework aims to provide not only accurate predictions on phishing susceptibility but also explanations of why an individual fell victim to a phishing attempt. In addition, it offers personalized recommendations on how to effectively minimize the risk of potential future phishing attacks. The present study builds upon two prior research studies [2; 10] that focused on identifying the most important factors associated with susceptibility to phishing attacks at a broader scale. While these studies yielded valuable insights, their results lack the specificity needed to offer personalized guidance to individuals in reducing their vulnerability to phishing attempts. Notably, these results are derived at a population level and may not account for the unique circumstances of individuals. In contrast, our current research aims to pinpoint the key factors associated with susceptibility to phishing attacks at the individual level, leveraging an XAI method. Our primary goal is to offer personalized guidance to individuals, empowering them to proactively reduce their risk of succumbing to phishing attacks. In addition, we aim to complement and extend the findings of prior phishing studies through our approach. Furthermore, we seek to enhance the understanding of the diverse factors influencing phishing susceptibility, enabling the development of more tailored and effective educational and preventive measures. Another objective is to contribute to the broader discourse on cybersecurity awareness, particularly in the context of evolving phishing tactics. The contributions of this paper are summarized as follows:  
1. To the best of our knowledge, this is the first attempt at employing XAI techniques to analyze susceptibility to phishing attacks.