Explainable AI-Based Real-Time Anomaly Detection for Cloud Environments

An explainable AI-based real-time anomaly detection system for cloud environments.

An explainable AI-based real-time anomaly detection system for cloud environments. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to understand networks and cloud systems: building or simulating the environment, measuring performance and comparing approaches.

The main parts are explanations of each result (feature importance or visual evidence), real-time processing of incoming data, a simulated or deployed environment, the algorithm or mechanism being studied, and performance measurement against baselines. The reference build uses SHAP, Apache Kafka, Docker, Python, and Linux, with a clear project structure, setup instructions and sample data so that it can be run and extended.