# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 5 Conclusions  
Detecting phishing websites necessitates a strong and adaptable methodology that can handle the changing nature of phishing attacks. The traditional categorization methods are ineffective in modern circumstances because they cannot adapt to new patterns and behaviors. In this paper, we overcome these problems by presenting a hybrid deep learning-based model based on GCNs. The suggested methodology employs advanced techniques, such as feature extraction using GCNs, Manhattan similarity for graph creation, and the hinge loss for exact classification. This technique increases not only the feature selection but also the overall accuracy and adaptability of phishing detection systems. To gain deeper insights into the model's performance, we analyzed the misclassified instances. Our observations indicate that most misclassifications occurred for borderline cases or obfuscated phishing sites, which share many characteristics with legitimate sites, making them inherently challenging to detect. This highlights potential areas for future improvement, such as incorporating more sophisticated feature representations or additional context to better distinguish these difficult cases. With future advances, these hybrid models have the potential to greatly improve online security and more successfully combat phishing attacks. In the future, incorporating real-time data streams and continuous learning methods into the model could allow it to respond more quickly to developing phishing patterns. Extending the feature extraction method to include advanced graph-based algorithms like hypergraph neural networks may increase the detection accuracy even more. Furthermore, investigating ensemble methods that mix different machine learning models may improve the reliability of phishing detection systems. The proposed method, which combines a GNN and an SVM, detects phishing with high accuracy and enhances the data resolution by selecting effective features and taking into account feature interactions. This strategy is scalable and effectively reduces the effects of uneven data. However, it faces several obstacles, including high computational complexity, the necessity for accurate hyperparameter tweaking, and the risk of overfitting. Furthermore, the model's implementation necessitates significant computer resources, and the optimal number of features is directly proportional to its performance.  
Conceptualization, L.M.K. and H.E.; methodology, S.S.S. and H.E.; software, S.S.S.; validation, S.S.S.; formal analysis, S.S.S.; investigation, H.E.; resources, H.E.; data curation, S.S.S.; writing--original draft preparation, S.S.S.; writing--review and editing, L.M.K. and H.E.; visualization, S.S.S.; supervision, L.M.K.; project administration, L.M.K.; funding acquisition, L.M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on reasonable request from the corresponding author. The data are not publicly available due to institutional data use policies.  
The authors declare no conflict of interest.