INTELLIGENT API THREAT DETECTION USING RUNTIME TRAFFIC ANALYTICS AND BEHAVIORAL MODELING

Abstract

Application Programming Interfaces (APIs) are the backbone of modern cloud-native enterprise applications, making them prime targets for cyberattacks such as credential abuse, API misuse, denial-of-service attacks, data exfiltration, and automated bot activity. Traditional signature-based security mechanisms are insufficient for identifying sophisticated runtime attacks that continuously evolve in distributed environments. This paper proposes an intelligent API threat detection framework integrating runtime traffic analytics, behavioral modeling, machine learning, OpenAPI Specification, cloud-native DevSecOps, API gateways, and continuous security monitoring. The proposed methodology continuously analyzes API traffic patterns, detects behavioral anomalies, identifies malicious activities, and automatically enforces adaptive security policies throughout the API lifecycle. Experimental evaluation demonstrates improvements in threat detection accuracy, runtime security, governance efficiency, operational scalability, and enterprise cyber resilience. The proposed framework provides an intelligent, scalable, and productionready solution for securing cloud-native enterprise APIs against advanced cyber threats. Keywords— API Threat Detection, Runtime Analytics, Behavioral Modeling, Machine Learning, API Security, Cloud-Native Security, DevSecOps, Enterprise Cybersecurity.