# Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
## 2 Related Literature
### ML and XAI in Phishing Study  
Several research works have applied machine learning techniques for phishing studies. [PERSON] et al. [25] utilized cluster analysis and an elaborate controlled experiment involving a large number of participants to identify and analyze user segments with high susceptibility to phishing, based on their demographics, perceptions, and behavior on phishing websites. [PERSON] et al. [26] developed a model for predicting phishing victims by proposing a multidimensional phishing susceptibility prediction model. They used seven supervised learning techniques to forecast the vulnerability of the enlisted volunteers, based on their demographic, personality, knowledge experience, and security behavior, all of which were obtained through a questionnaire. [PERSON] et al. [27] presented a user phishing susceptibility prediction model that incorporates both dynamic and static features. The model examines the impact of static factors, such as demographics, knowledge, and experience, as well as dynamic factors, such as design changes and eye tracking, on user susceptibility. To predict susceptibility accurately, a hybrid prediction model that combines Long Short-Term Memory (LSTM) and LightGBM was developed, resulting in a prediction accuracy of 92.34 percent. [PERSON] et al. [28] proposed a conditional generative adversarial network (C-GAN) model for both classification and data generation to find the potential associations between personality traits and phishing attacks. [PERSON] et al. [29] proposed a new approach that integrates cognitive modeling and machine learning to enhance training effectiveness. To select appropriate targets for intervention during the training process, they utilized a restless multi-armed bandit framework and incorporated a cognitive model of phishing susceptibility to inform the bandit model's parameters. Other studies use machine learning techniques to detect phishing webpages or emails. They apply different approaches to extract phishing classification information from diverse sources, such as visual information like logos [30; 31; 32; 33], textual information like URLs [34; 35; 36; 37; 38; 39; 40; 41; 42; 43], and webpage content [44; 45]. As the present paper focuses on human factors associated with phishing susceptibility, readers interested in applying machine learning for phishing detection are directed to surveys by [PERSON] and [PERSON] [46] and [PERSON] et al. [47] for more details. To our knowledge, only a limited number of studies have employed XAI techniques to investigate the phenomenon of phishing. [PERSON] et al. [48] proposed an XAI approach for phishing detection using URL-based features. The authors used machine learning models along with various XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and explainable boosting machine (EBM) to identify the most important URL features contributing to the model's prediction. Their results show that the most important URL features identified by the XAI techniques are consistent with common phishing characteristics. [PERSON] et al. (2019) proposed a multi-modal hierarchical attention model for developing meaningful phishing detection systems. The model includes two levels of attention mechanisms to enable the extraction of relevant features and informative interpretability across multiple levels. [PERSON] et al. (2018) proposed a hybrid deep learning-based approach called Phishpedia to visually identify phishing webpages with explainable visual annotations on the phishing page screenshot. Recently, [PERSON] and [PERSON] (2019) proposed a user-focused anti-phishing measure that leverages XAI to improve users' understanding of the cues that contribute to the suspicion of phishing and uncover the words and phrases in an e-mail that are most relevant for identifying phishing attempts. The summary of the ML and XAI-based phishing studies is depicted in Table 1.