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Original Article
Phishing GAT: Adversarial-Hardened Phishing Email Detection via Semantic-Structural Fusion and Graph Attention Networks
Kodali Rohith1
Dr. Siva Rama Krishna T2
1 PG Scholar, Department of Computer Science and Engineering, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, India. 2 Associate Professor, Department of Computer Science and Engineering, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, India.
Published Online: July-August 2026
Pages: 112-125
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260704015
References
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of Engineering, VTU, 2024, doi: 10.13140/RG.2.2.22583.84644.
12. R. S. Al-Yozbaky and M. Alanezi, “Detection and analyzing phishing emails using NLP techniques,” in Proc. 5th Int. Congr. Human-
Computer Interaction, Optimization and Robotic Applications (HORA), 2023, pp. 1–6, doi: 10.1109/HORA58378.2023.10156738.
13. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” 2019,
doi: 10.48550/arXiv.1910.01108.
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10.48550/arXiv.2204.02685.
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17. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. Int. Conf. Learning
Representations (ICLR), 2017, doi: 10.48550/arxiv.1609.02907.
18. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. Int. Conf. Learning
Representations (ICLR), 2018, doi: 10.48550/arxiv.1710.10903.
19. S. Brody, U. Alon, and E. Yahav, “How attentive are graph attention networks?” in Proc. Int. Conf. Learning Representations (ICLR),
2022, doi: 10.48550/arxiv.2105.14491.
20. W. Guo, Q. Wang, H. Yue, H. Sun, and R. Q. Hu, “Efficient phishing URL detection using graph-based machine learning and loopy
belief propagation,” 2025, doi: 10.48550/arXiv.2501.06912.
21. M. Saranya, D. L. Pansy, and V. M. Thejashree, “Smart email filtering against phishing attacks,” Journal of Cybersecurity, 2025, doi:
10.1109/ICDSAAI65575.2025.11011707.
22. N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proc. IEEE Symp. Security and Privacy (S&P),
2017, pp. 39–57, doi: 10.1109/SP.2017.49.
23. T. Koide, N. Fukushi, H. Nakano, and D. Chiba, “Leveraging large language models for effective phishing email detection,” 2024, doi:
10.48550/arXiv.2402.18093.
24. P. Chinta et al., “AI-powered phishing detection: A data-driven cybersecurity approach,” IEEE Access, 2025, doi:
10.1109/ICDSAAI65575.2025.11011572.
25. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global vectors for word representation,” in Proc. Conf. Empirical Methods in
Natural Language Processing (EMNLP), 2014, pp. 1532–1543, doi: 10.3115/v1/D14-1162.
26. D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, “Robustness may be at odds with accuracy,” in Proc. Int. Conf.
Learning Representations (ICLR), 2019, doi: 10.48550/arxiv.1805.12152
27. R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “GNNExplainer: Generating explanations for graph neural networks,” in
Proc. Advances in Neural Information Processing Systems (NeurIPS), 2019, pp. 9240–9251, doi: 10.48550/arXiv.1903.0389.
https://www.statista.com/statistics/456500.
2. M. Khalid et al., “SpearBot: Leveraging large language models in a generative-critique framework for spear-phishing email generation
and detection,” Computers & Security, 2024, doi: 10.1016/j.inffus.2025.103176
3. F. Heiding, B. Schneier, A. Vishwanath, J. Bernstein, and P. S. Park, “Devising and detecting phishing emails using large language
models,” IEEE Access, vol. 12, pp. 42131–42146, 2024, doi: 10.1109/ACCESS.2024.3375882.
4. S. Atawneh and H. Aljehani, “Phishing email detection model using deep learning,” Electronics, 2023, doi:
10.3390/electronics12204261.
5. P. C. R. Chinta, C. S. Moore, L. M. Karaka, M. Sakuru, V. Bodepudi, and S. R. Maka, “Building an intelligent phishing email detection
system using machine learning and feature engineering,” European Journal of Applied Science, Engineering and Technology, vol. 3,
no. 2, pp. 41–54, 2025, doi: 10.59324/ejaset.2025.3(2).04.
6. D. Sturman et al., “Evaluating the performance of machine learning and deep learning models across multiple datasets for phishing
email detection,” IEEE Access, 2025, doi: 10.3390/app15063396.
7. M. Safran and A. Musleh, “PhishingGNN: Phishing email detection using graph attention networks and transformer-based feature
extraction,” IEEE Access, vol. 13, pp. 131390–131399, 2025, doi: 10.1109/ACCESS.2025.3592135.
8. D. Zügner, A. Akbarnejad, and S. Günnemann, “Adversarial attacks on neural networks for graph data,” in Proc. 24th ACM SIGKDD
Int. Conf. Knowledge Discovery & Data Mining (KDD), 2018, pp. 2847–2856, doi: 10.1145/3219819.3220078.
9. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in Proc.
Int. Conf. Learning Representations (ICLR), 2018, doi: 10.48550/arxiv.1706.06083.
10. I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in Proc. Int. Conf. Learning
Representations (ICLR), 2015, doi: 10.48550/arxiv.1412.6572.
11. H. Kousar, B. Jenifer, A. Khan, Chaithra G, and Sumaiya, “Phishing email detection using random forest technique,” HKBK College
of Engineering, VTU, 2024, doi: 10.13140/RG.2.2.22583.84644.
12. R. S. Al-Yozbaky and M. Alanezi, “Detection and analyzing phishing emails using NLP techniques,” in Proc. 5th Int. Congr. Human-
Computer Interaction, Optimization and Robotic Applications (HORA), 2023, pp. 1–6, doi: 10.1109/HORA58378.2023.10156738.
13. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” 2019,
doi: 10.48550/arXiv.1910.01108.
14. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language
understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186, doi: 10.18653/v1/N19-1423.
15. E. Aghaei, X. Niu, W. Shadid, and E. Al-Shaer, “SecureBERT: A domain-specific language model for cybersecurity,” 2022, doi:
10.48550/arXiv.2204.02685.
16. S. Nair et al., “Hybrid machine learning approach for securing emails: Phishing detection and prevention,” Computers & Security,
2025, doi: 10.1109/ICAECA63854.2025.11012305.
17. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. Int. Conf. Learning
Representations (ICLR), 2017, doi: 10.48550/arxiv.1609.02907.
18. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proc. Int. Conf. Learning
Representations (ICLR), 2018, doi: 10.48550/arxiv.1710.10903.
19. S. Brody, U. Alon, and E. Yahav, “How attentive are graph attention networks?” in Proc. Int. Conf. Learning Representations (ICLR),
2022, doi: 10.48550/arxiv.2105.14491.
20. W. Guo, Q. Wang, H. Yue, H. Sun, and R. Q. Hu, “Efficient phishing URL detection using graph-based machine learning and loopy
belief propagation,” 2025, doi: 10.48550/arXiv.2501.06912.
21. M. Saranya, D. L. Pansy, and V. M. Thejashree, “Smart email filtering against phishing attacks,” Journal of Cybersecurity, 2025, doi:
10.1109/ICDSAAI65575.2025.11011707.
22. N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proc. IEEE Symp. Security and Privacy (S&P),
2017, pp. 39–57, doi: 10.1109/SP.2017.49.
23. T. Koide, N. Fukushi, H. Nakano, and D. Chiba, “Leveraging large language models for effective phishing email detection,” 2024, doi:
10.48550/arXiv.2402.18093.
24. P. Chinta et al., “AI-powered phishing detection: A data-driven cybersecurity approach,” IEEE Access, 2025, doi:
10.1109/ICDSAAI65575.2025.11011572.
25. J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global vectors for word representation,” in Proc. Conf. Empirical Methods in
Natural Language Processing (EMNLP), 2014, pp. 1532–1543, doi: 10.3115/v1/D14-1162.
26. D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, “Robustness may be at odds with accuracy,” in Proc. Int. Conf.
Learning Representations (ICLR), 2019, doi: 10.48550/arxiv.1805.12152
27. R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “GNNExplainer: Generating explanations for graph neural networks,” in
Proc. Advances in Neural Information Processing Systems (NeurIPS), 2019, pp. 9240–9251, doi: 10.48550/arXiv.1903.0389.
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