Research and Development of a Smart Solution for Runtime Web Application Self-Protection
Phuc Le-Thanh, Tuan Le-Anh, Quan Le‐Trung · 2023
In contemporary times, ensuring web application security is a critical concern for organizations due to the prevalence of numerous types of attacks that serve diverse purposes. While traditional security measures such as web application firewalls (WAF) and intrusion detection systems (IDS) can help mitigate attacks, there is still a possibility of them being circumvented or compromised. A more efficacious approach is to adopt runtime application self-protection (RASP) solutions integrated within the web application. This solution has demonstrated its effectiveness by aiding in early attack detection and rapid attack mitigation. In this research, we propose a smart solution for runtime web application self-protection (RASP) to protect against vulnerabilities, attacks, and common weaknesses that have been rated among the top ten web security risks in 2021 by the Open Web Application Security Project (OWASP). The proposed solution leverages convolutional neural network (CNN) and a family of recurrent neural network (RNN) techniques. It builds a deep learning model with deep neural network architectures that scrutinizes user requests, thereby detecting potential SQL injection (SQLi), Cross-Site scripting (XSS), command injection (CMDi), and other types of attacks. The solution is designed to dynamically adapt to the application’s behavior and traffic, with the goal of minimizing false positives and preventing the blocking of legitimate traffic. Furthermore, the proposed solution, based on a microservices architecture, enhances the flexibility of the prediction module during upgrades and automated deployment. It is integrated with MLOps and DevSecOps and is also designed to be compatible with RESTful API servers. Our results have validated the efficacy of this solution in providing real-time application protection.