Abstract: Server-side request forgery (SSRF) is a security vulnerability that arises from a vulnerability in web applications. For example, when the services are accessed via URL the attacker supply or modify a URL to access services on servers that he is not permitted to use. In this research, various types of SSRF attacks are discussed, and how to secure web applications are explained. Various techniques have been used to detect and mitigate these attacks, most of which are concerned with the use of machine learning techniques. The main focus of this research was the application of deep learning techniques (LSTM networks) to create an intelligent model capable of detecting these attacks. The generated deep learning model achieved an accuracy rate of 0.969, which indicates the strength of the model and its ability to detect SSRF attacks.

Khadejah Al-talak and Onytra Abbass, “Detecting Server-Side Request Forgery (SSRF) Attack by using Deep Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121230

@article{Al-talak2021,
title = {Detecting Server-Side Request Forgery (SSRF) Attack by using Deep Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121230},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121230},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Khadejah Al-talak and Onytra Abbass}
}