Intelligent API Threat Detection and Behavioral Traffic Analysis for Microservices-Based Enterprise Applications

Abstract

The increasing adoption of microservices architecture has transformed enterprise application development by enabling modular services, independent deployment, scalability, and flexible communication through Application Programming Interfaces (APIs). However, the extensive use of interconnected APIs introduces significant security challenges, including unauthorized access, abnormal traffic patterns, API abuse, injection attempts, authentication failures, and distributed denial-of-service attacks. Traditional security mechanisms primarily depend on predefined rules and signature-based detection, which may be insufficient for identifying evolving threats and sophisticated behavioral anomalies. This research proposes an intelligent API threat detection and behavioral traffic analysis framework for microservices-based enterprise applications using machine learning and behavioral analytics. The proposed framework analyzes API request characteristics, traffic patterns, authentication activities, request frequencies, response behaviors, and service communication relationships to identify potentially malicious activities. Machine learning techniques are employed to establish normal API behavioral profiles and detect deviations associated with suspicious traffic. Furthermore, an intelligent threat classification mechanism categorizes detected activities according to their severity, behavioral characteristics, and potential operational impact. The framework also incorporates real-time monitoring, anomaly detection, and automated security alert generation to improve API protection across distributed enterprise environments. The proposed approach aims to enhance threat detection accuracy, reduce false-positive alerts, improve visibility into API communication patterns, and support proactive security management in modern microservices-based applications.