TwinShield is a sophisticated fraud detection system that leverages MongoDB and machine learning to identify suspicious transactions in real-time. Key components of the system include:
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Digital Twin Concept: Each user has a behavioral profile stored as a document in MongoDB, which evolves dynamically based on transaction history.
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Machine Learning Model: An Isolation Forest model is used for anomaly detection. It scores each transaction and categorizes it into low, medium, or high risk based on the score.
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Fallback Rule Engine: A Java-based rule engine ensures system reliability by providing a fallback mechanism if the machine learning service fails.
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Simulation Engine: Injects various attack patterns to test the system's detection capabilities in real-time scenarios.
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Dashboard Interface: Provides live updates on transaction counts, anomaly rates, and user risk scores through an eight-second polling interval.
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Schema Flexibility: MongoDB’s document model allows for easy schema evolution without requiring migration scripts.
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Future Enhancements:
- Integration of MongoDB Aggregation Pipelines to analyze time-series data.
- Exploration of Atlas Vector Search for semantic similarity queries across past transactions.
- Automated retraining pipeline using fresh MongoDB data to update the Isolation Forest
Read the full article at DEV Community
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