This project involves the development of an AI-powered adaptive security gateway designed to enhance the protection of API infrastructures. By leveraging machine learning for real-time anomaly detection, the system moves beyond traditional static rule-based security mechanisms. It aims to identify and mitigate novel and evolving cyber threats, providing a resilient security layer for modern web applications and microservices architectures.

The primary goal is to design, prototype, and evaluate an automated security gateway that uses machine learning to detect anomalous API traffic in real-time, thereby improving the security and resilience of network-based API services against advanced and zero-day attacks.

Progress will be monitored through weekly reviews against the project plan. Code will be version-controlled via Git. Regular checkpoints will include design approvals, model validation results, and prototype demonstrations. A final evaluation will assess functional compliance and performance benchmarks.

The results will be presented in the form of a working prototype demonstration, a written thesis document, and a presentation summarizing the project’s design, implementation, testing, and findings.

The project will require computational resources for development and testing, including a server environment for deploying the gateway, a Redis instance for real-time caching, and cloud/VM resources for scalable testing. Development tools such as Python, FastAPI, Scikit-learn, Docker, and Git will be used. Access to benchmark datasets (e.g., CSIC 2010) for model training and validation is also required.

The student has prior experience in Python programming, machine learning, and network security concepts. Available resources include a personal development machine, access to university cloud/lab servers, and existing knowledge of Docker, Git, and relevant Python libraries (FastAPI, Scikit-learn, Pandas, NumPy).