# Adversarial Training for Mitigating Insider-Driven XAI-Based Backdoor Attacks
## 3 Proposed Approach
### Training Models  
Recent works have investigated the effectiveness of deep learning models on tabular data and concluded that regularized networks could handle tabular data well [34]. This work employs DL tabular learning models to perform multi-class classification. Much research has been carried out to develop transformer architectures that can process huge tabular datasets successfully since transformer architectures were first introduced to tabular data [35]. Similarly, new approaches use self-normalized neural networks (SNNs) [36]. We propose deep learning models adopting the self-normalizing neural network on a conventional Multi-Layer Perceptron (MLP) and one-dimensional 1 DCNN. The features of the training data are strengthened by the use of scaled exponential linear units (SeLUs). Activation can spread through the network's layers while maintaining normalization due to self-normalizing features. SeLUs can thus preserve the network's stability and convergence while enhancing the model's generalization abilities. The details of the proposed architectures are explained in the following sections. **SNN-MLP architecture:** We designed the SNN-MLP as a set of fully connected layers followed by a classification layer. There are four fully connected layers with \(512,256,128,64\) neurons. The LeCun uniform initializer is used to initialize the layers and has an alpha-dropout rate of \(0.05\). Also, the bias initializer is set to zeroes. The classification layer is a dense layer with neurons equal to the number of classes and softmax activation. **SNN-1 DCNN architecture:** The model uses two 1D convolutional layers with SeLU activation and batch normalization to learn the local patterns and extract the features. One-dimensional convolutional layers are more suitable for tabular data. The LeCun uniform initializer is used to initialize the convolutional kernel size; the pooling kernel size is set to \(2\). Following these 1D layers, three fully connected layers with \(64,32,16\) neurons are added with an alpha-dropout rate of \(0.2\), the optimizer is Adam, the iteration number is set to \(100\), and the batch size for each training round is \(1024\). Finally, there is a classification layer with the number of neurons equal to the number of classes in the dataset and a softmax activation to perform multiclass classification. The performance of the model, determined experimentally in this study, depends critically on the size and step length of the convolutional kernels and the number of layers and convolutional kernels. The 1 DCNN with SeLUs is used in fault diagnosis applications and has proved useful in combating the overfitting issue [37]. Hence, adopting a similar combination in this work, the issue of overfitting the model is successfully tackled by combining the 1 DCNN with the self-normalized neural network, SNN, and utilizing the self-normalization property of the SeLU activation function. In the existing works, ReLU activation is used; this makes the output of neurons with a negative input value zero, thereby reducing the interdependence among parameters. However, the SeLU activation used in SNN preserves the positive and negative values and is known to learn faster. The alpha-dropout layer is used to regularize the training process. The dropout layer added to the network improves the performance and improves generalization. Better generalization capability of the model results in reduced overfitting. **TabNet:** This study also employs the TabNet [35] model, which maintains the end-to-end and representation learning capabilities of a DNN. Based on its qualities, it also has the benefits of interpretability from the tree model and sparse feature selection, making it comparable to the widely used tree model currently used in the table data task and having the benefits of a DNN. The model improves the network's interpretability while also increasing the model performance. TabNet can allow different samples to select different features, as the sample mask vector can change.