# Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
## 3 Materials and Methods
### Deep Neural Networks  
Deep neural networks, also known as deep learning models, are a class of neural networks that consist of multiple layers of interconnected artificial neurons. These networks are designed to learn hierarchical representations of data by progressively extracting more abstract and complex features as information flows through the layers. Unlike shallow neural networks with only one or a few hidden layers, deep neural networks have a greater depth, typically comprising multiple hidden layers. Each layer consists of a set of neurons that perform computations on the input data and pass the results to the next layer. The output of one layer serves as the input to the subsequent layer, enabling the network to learn increasingly sophisticated representations. The depth of deep neural networks allows them to capture intricate patterns and relationships in data, making them particularly effective in handling large and complex datasets. Deep learning models have shown remarkable success in various domains, including computer vision, natural language processing, speech recognition, and reinforcement learning. Deep learning models, in comparison to traditional models in phishing susceptibility studies, offer a more complex and potentially higher-performing alternative. The model can capture nuanced patterns in phishing tactics that simpler models might miss. The strength of the model lies in automatic feature detection and creation, allowing them to adapt effectively to varied data types. This adaptability is particularly beneficial in the evolving landscape of phishing, where new threats constantly emerge. While they do require substantial computational resources, the potential for higher accuracy and better generalization to new, unseen data makes deep neural networks a powerful tool in the arsenal against phishing attacks, especially in scenarios where dataset size and diversity are sufficient. We trained a multi-layer perceptron (MLP) neural network to make predictions. The neural network architecture used in the current study consists of four hidden layers. The first two layers have 256 neurons each and were followed by a rectified linear unit (ReLU) activation function. The third layer has 128 neurons and is followed by a hyperbolic tangent (Tanh) activation function. The fourth layer has 32 neurons with ReLU activation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Name** & **Value Type** & **Description** \\ \hline Check Link & \multirow{2}{*}{Numeric} & Response to the corresponding survey question in Section 3. Range from 1.0 (never) to 5.0 (very often) \\ (checklink) & & 1.0 (never) to 5.0 (very often) \\ \hline Privacy Setting & \multirow{2}{*}{Numeric} & Response to the corresponding survey question in Section 3. Range from 1.0 (never) to 5.0 (very often) \\ (privacysetting) & & 1.0 (never) to 5.0 (very often) \\ \hline Check HTTPS & \multirow{2}{*}{Numeric} & Response to the corresponding survey question in Section 3. Range from 1.0 (never) to 5.0 (very often) \\ (checkhttps) & & 1.0 (never) to 5.0 (very often) \\ \hline Click w/o Check & \multirow{2}{*}{Numeric} & Response to the corresponding survey question in Section 3. Range from 1.0 (never) to 5.0 (very often) \\ (clickwocheck) & & 1.0 (never) to 5.0 (very often) \\ \hline Phished Before & \multirow{2}{*}{Binary} & Response to the corresponding survey question in Section 3. Binary valued: 1.0 (pushlist3 mon) & Yes = 1, No = 0 \\ \hline Phished in Last 3 Months & \multirow{2}{*}{Binary} & Response to the corresponding survey question in Section 3. Binary valued: 1.0 (loseinfo) & Yes = 1, No = 0 \\ \hline  & & \\ \hline  & & \\ \end{tabular}
\end{table} 
Table 4: _Cont._The output layer is dense with a sigmoid activation function. Figure 1 below depicts the network architecture. The purpose of using an MLP neural network is to learn a non-linear mapping between the input data and the target variable.