A Machine Learning Framework for Robust Java API Security: Enhancing Vulnerability Detection and Mitigation

Abstract

Securing Java APIs is vital in today's interconnected world. APIs are gateways for data exchange. They face constant threats, such as unauthorized access and malicious payloads. With the rise of artificial intelligence (AI) and machine learning (ML), traditional security measures like HTTPS, OAuth 2.0, and JSON Web Tokens (JWT) can now integrate advanced threat detection. AI and ML offer real - time anomaly detection, token validation, and communication layer protection. They help identify threats earlier, reducing the risks of data breaches. This article looks at the role of AI and ML in enhancing Java API security. It discusses their use in detecting threats, ensuring token integrity, and maintaining secure communication. These technologies strengthen API defense mechanisms, enabling safer and more reliable digital ecosystems.