AI-Powered API Gateways for Adaptive Rate Limiting and Threat Detection
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P112Keywords:
AI-Powered, API Gateways, Adaptive Rate Limiting, Threat Detection, Artificial Intelligence, API Security, Rate Limiting, Anomaly DetectionAbstract
Digital ecosystems and apps built with microservices are growing very quickly, and API gateways have become very important for managing their growth and keeping them secure. Traditional methods like rate limiting and intrusion detection often don’t work well when dealing with changing traffic or advanced threats like DDoS attacks, unusual traffic spikes, and zero-day attacks. This paper introduces an AI-powered API gateway framework that uses rate limiting and advanced attack detection to solve these problems. The system uses machine learning to spot unusual activity, reinforcement learning to update policies in real time, and profiles the behavior of API requests to create a self-learning, self-adjusting security system. Testing this system on cloud-based microservice setups shows it handles more traffic faster, has lower delays, and detects threats more accurately than traditional methods. The results show a big improvement in reducing false alarms and missed threats, and it can stop attacks quickly, even when many requests are coming in at once. The system also works well with zero-trust security models, making it suitable for large businesses. Combining adaptability, smart features, and strong protection makes AI-driven API gateways a promising solution for securing digital systems in a rapidly changing threat landscape. The findings show that these gateways not only improve security but also make systems run more efficiently, making them essential for modern, large-scale applications
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