Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks

Saif Safaa Shakir, Leyli Mohammad Khanli and Hojjat Emami ()
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Saif Safaa Shakir: Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Leyli Mohammad Khanli: Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran
Hojjat Emami: Department of Computer Engineering, Faculty of Engineering, University of Bonab, Bonab 5551395133, Iran

Future Internet, 2025, vol. 17, issue 8, 1-19

Abstract: Phishing attacks pose significant risks to security, drawing considerable attention from both security professionals and customers. Despite extensive research, the current phishing website detection mechanisms often fail to efficiently diagnose unknown attacks due to their poor performances in the feature selection stage. Many techniques suffer from overfitting when working with huge datasets. To address this issue, we propose a feature selection strategy based on a convolutional graph network, which utilizes a dataset containing both labels and features, along with hyperparameters for a Support Vector Machine (SVM) and a graph neural network (GNN). Our technique consists of three main stages: (1) preprocessing the data by dividing them into testing and training sets, (2) constructing a graph from pairwise feature distances using the Manhattan distance and adding self-loops to nodes, and (3) implementing a GraphSAGE model with node embeddings and training the GNN by updating the node embeddings through message passing from neighbors, calculating the hinge loss, applying the softmax function, and updating weights via backpropagation. Additionally, we compute the neighborhood random walk (NRW) distance using a random walk with restart to create an adjacency matrix that captures the node relationships. The node features are ranked based on gradient significance to select the top k features, and the SVM is trained using the selected features, with the hyperparameters tuned through cross-validation. We evaluated our model on a test set, calculating the performance metrics and validating the effectiveness of the PhishGNN dataset. Our model achieved a precision of 90.78%, an F1-score of 93.79%, a recall of 97%, and an accuracy of 93.53%, outperforming the existing techniques.

Keywords: deep learning; feature extraction; feature selection; website phishing detection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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