Abstract: Web applications are part of the daily life of Internet users, who find services in all sectors of activity. Web applications have become the target of malicious users. They exploit web application vulnerabilities to gain access to unauthorized resources and sensitive data, with consequences for users and businesses alike. The growing complexity of web techniques makes traditional web vulnerability detection methods less effective. These methods tend to generate false positives, and their implementation requires cybersecurity expertise. As for Machine Learning/Deep Learning-based web vulnerability detection techniques, they require large datasets for model training. Unfortunately, the lack of data and its obsolescence make these models inoperable. The emergence of large language models and their success in natural language processing offers new prospects for web vulnerability detection. Large language models can be fine-tuned with little data to perform specific tasks. In this paper, we propose an approach based on large language models for web application vulnerability detection.

Sidwendluian Romaric Nana, Didier Bassole, Desire Guel and Oumarou Sie. “Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507135

@article{Nana2024,
title = {Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507135},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507135},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Sidwendluian Romaric Nana and Didier Bassole and Desire Guel and Oumarou Sie}
}