Proactive Threat Mitigation in API Ecosystems through AI-Powered Anomaly Detection
DOI:
https://doi.org/10.15662/IJARCST.2023.0601004Keywords:
API Security, Anomaly Detection, Artificial Intelligence, Proactive Threat Mitigation, Machine Learning, Deep Learning, Cybersecurity, Real-time Monitoring, API Ecosystems, Zero-day AttacksAbstract
Application Programming Interfaces (APIs) have become the backbone of digital ecosystems, enabling seamless connectivity across cloud platforms, mobile applications, and enterprise systems. However, this rapid expansion has also introduced significant security challenges, as APIs are increasingly exploited as attack vectors. Traditional rule-based security mechanisms are often inadequate to detect sophisticated, zero-day, or behaviorally complex threats. This paper explores an AI-powered anomaly detection framework designed to proactively mitigate risks in API ecosystems. By leveraging machine learning to establish behavioral baselines and deep learning for contextual anomaly detection, organizations can detect and respond to abnormal traffic patterns in real time. Proactive threat mitigation strategies—such as predictive analytics, automated response workflows, and integration with Security Information and Event Management (SIEM) platforms—are discussed. The study highlights the effectiveness of AI in enabling adaptive, scalable, and intelligent defenses that safeguard critical digital infrastructures.
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