AI-Driven Automated Security Testing for Secure Protocols and Web Applications: A Comprehensive Framework Analysis

Abstract

This article presents a comprehensive analysis of automated testing frameworks for Transport Layer Security (TLS), QUIC, and secure protocols in modern network environments. The article examines the evolution of testing methodologies, highlighting the critical role of automation in addressing emerging cyber threats and protocol vulnerabilities. The article investigates core testing components, including OpenSSL integration and cURL implementations, while analyzing web server testing automation through Apache, Nginx, and Scapy frameworks. The article further explores framework architecture, emphasizing AI and machine learning integration for enhanced testing capabilities. Performance considerations, load testing architectures, and security aspects are thoroughly examined, providing insights into vulnerability assessment and compliance verification mechanisms. Ultimately, this paper demonstrates how automated frameworks significantly boost testing efficiency, improve the precision of vulnerability detection, and optimize resource utilization, collectively contributing to a stronger security posture and a measurable reduction in operational costs.