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This project demonstrates a complete, end-to-end real-time fraud detection system built with a modern streaming data stack. It simulates a stream of financial transactions, processes them using a stateful machine learning model in Apache Flink, and displays detected fraudulent activity on a live dashboard.
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fix(security): Upgrade pyarrow and protobuf to fix CVEs
9737271View on GitHubfeat(flink-app): Add security scanning to CI workflow
f4eb744View on GitHubfix(security): Upgrade fastapi to resolve starlette CVE
f80ea5fView on GitHubfeat(alert-monitor): Add security scanning and harden Dockerfile
75e90b3View on GitHubfix(security): Upgrade setuptools during build to fix CVEs
ae5de63View on GitHubfix(security): Upgrade base image to bookworm to fix CVEs
79e1707View on GitHubfeat(ci): Integrate Trivy vulnerability scanning for payment-api
20a54a7View on GitHubfeat(flink-app): Add linting quality gate to CI workflow
ff4f361View on GitHubrefactor(tests): Update WebSocket connection tests for clarity and accuracy
28c10e1View on GitHubrefactor(tests): Simplify WebSocket connection tests and improve broadcast assertions
49b0b01View on GitHubrefactor(tests): Update test client to use TestClient for synchronous tests
7e04917View on GitHub51
commits