Automated API Security Testing Tool

Abstract

With the rapid growth of web applications, Application Programming Interfaces (APIs) have become a fundamental component of modern software systems, making them increasingly vulnerable to cyber threats. According to OWASP, API security risks such as broken authentication, excessive data exposure, and injection attacks are among the top vulnerabilities in modern applications [1]. This project presents an Automated API Security Testing Tool, an intelligent system designed to detect and analyze security vulnerabilities in API traffic. The proposed system integrates both machine learning techniques and rule-based heuristic approaches to improve threat detection accuracy. Machine learning models are trained on historical API traffic data to identify anomalous patterns and classify risk levels, while heuristic rules are used to detect known vulnerabilities based on predefined security signatures [2]. This hybrid risk scoring mechanism enhances the reliability of security assessments. The tool captures API requests and responses through a monitoring mechanism and performs real-time analysis to identify potential threats. It generates alerts and detailed reports that assist developers and security analysts in mitigating risks effectively. Studies show that automated security testing combined with intelligent analysis significantly reduces manual effort and improves vulnerability detection rates [3].This project contributes to proactive cybersecurity by providing a scalable, efficient, and user-friendly solution for API security testing, ensuring better protection of modern web applications.