# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 2 Literature Review
This study focuses on using the framework to identify fake websites. ML classifiers can be applied for properly recognizing phishing websites. So, various ML classifiers, like HNB, Naive Bayes, and J48, have been applied here [35]. Despite the various phishing detection methods in the research literature, relatively few studies consider the feature selection strategy. This strategy removes unnecessary or irrelevant features for the problem of detecting phishing websites. Based on the importance of each feature for the detection accuracy, the authors of [36] investigated the key features for detecting phishing websites. A benchmark phishing dataset was used as a feature selection tool, and a gravitational search algorithm (GSA) was used. In [37], a public dataset was used, and a diagnostic mechanism was proposed to identify bad URLs applying recurrent neural network models like bidirectional long short-term memory (Bi-LSTM), gated recurrent units (GRUs), and LSTM. In [38], a developed binary bat mechanism version was applied for designing neural networks, which groups websites into phishing and non-phishing groups. Here, the DL-based swarm intelligence binary bat algorithm (SI-BBA) model is provided for diagnosing phishing websites. The binary bat mechanism was applied to setting the presented CNN network's hyperparameters. Various mechanisms of optimization were applied in the CNN network. In [39], a novel ML strategy for grouping phishing websites applying CNNs on URL-based features is provided. CNNs include a fully connected layer, a convolution stack, and pooling layers. For preventing the gradient-vanishing issue, recent CNNs apply entropy loss tasks with rectified linear units (ReLUs). For applying CNNs, the vectors of features are converted into images. In [40], the authors present a model for detecting phishing scams that is enriched by the community. They propose a methodology for detecting network phishing using graph neural networks. First, they created an Ethereum transaction network and extracted transaction subgraphs as well as related content components. They provide a diagnosis strategy given the community-developed GCN. The algorithm improves the node representation in GCN neighborhoods and investigates graph semantics through community structure and node similarity measurements. In [41], the authors employ a GCN. The authors discovered that the network is highly heterophilic and models Ethereum transaction records as a large-scale transaction network, with accounts with varying features and labels connected. To address this issue, we propose a GCN-based model termed the EH-GCN. In [42], a multiscale feature fusion technique is used with a graph convolutional network model to detect phishing frauds. As a result, in the edge-embedding representation module, all the transaction times and values between two nodes are classified, and a gate recurrent unit (GRU) neural network is developed to obtain temporal features in the order of transactions, resulting in a representation of the fixed-length edge embedding from variable-length input. Weights of attention are defined for complete embedding representations surrounding a node, collecting edge-embedding representations and structural relations into a node in the time-trading feature module. At last, primary and time-trading node features, graph attention networks (GATs), GCNs, and SAGEConv are integrated and used for grouping nodes of phishing. The related works in phishing detection are summarized in Table 1.