Framework For The Detection And Mitigation Of Web Vulnerabilities Using Deep Learning
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 12 |
| Year of Publication : 2025 |
| Authors : Godwin Ponsam J, Chin Shiuh Shieh, V Senthil Murugan |
10.14445/23488549/IJECE-V12I12P114
How to Cite?
Godwin Ponsam J, Chin Shiuh Shieh, V Senthil Murugan, "Framework For The Detection And Mitigation Of Web Vulnerabilities Using Deep Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 12, pp. 164-176, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P114
Abstract:
Web vulnerability faces significant challenges, including data breaches, privacy violations, and financial losses. Comparing it with traditional conventional methods, it proves inadequate for identifying attack patterns and complex semantic structures in the temporal evolution of web page changes. This study primarily focuses on the IBERT-GRU model. To improve the detection and resolution of web vulnerabilities, the Integrated Bidirectional Encoder Representations from Transformers with Gated Recurrent Unit (IBERT-GRU) is enfolded. The IBERT model should incorporate the intricate semantic relationships and contextual information derived from diverse internet sources, including source code, network requests, and system logs. This method is considered an effective method for detecting patterns and revealing the weaknesses of the sequences. The proposed methodology is found to be more accurate (99.9%) and has a higher recall (97.2%) than benchmarked algorithms. The proposed method, in addition, has a better F1 score of 99.85%. The performance parameters indicate that the proposed IBERT-GRU architecture is a strong and scalable technique to keep track of vulnerabilities in real time in complicated online systems.
Keywords:
Web Vulnerability Detection, Deep Learning, IBERT-GRU, Transformer Models, Gated Recurrent Unit (GRU), Cybersecurity, Semantic Analysis.
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