This project implements a real-time fraud detection system for cryptocurrency trading using Go. The system fetches price data from the different sources, for instance Binance, CoinGecko, builds an isolation forest for anomaly detection, and reports detected anomalies along with statistics. The system is designed to run continuously, fetching new data at regular intervals and processing it to detect potential fraudulent activities.
- Enabled Sources: Perform operations based on enabled sources.
- Real-time Data Fetching: Continuously fetches enabled source cryptocurrency price data, e.g., Binance, CoinGecko.
- Isolation Forest: Implements an isolation forest for anomaly detection.
- Anomaly Detection: Detects anomalies in the price data and reports them.
- Statistics Reporting: Provides statistics on the total number of items and anomalies detected.
To install and run this project, follow these steps:
-
Clone the repository:
git clone https://github.com/mdshahjahanmiah/trading-fraud-detection.git cd trading-fraud-detection -
Run and Test the application::
go mod tidy go run cmd/main.go
The main entry point of the application is the main.go file. The application fetches price data, builds an isolation forest, detects anomalies, and reports them through channels.
main.go: Entry point of the application.source/source.go: Contains the source e.g., BinanceSource struct and methods for fetching price data.procedure/isolation_forest.go: Contains functions for building the isolation forest.detect/anomalies.go: Contains functions for detecting anomalies.
An isolation forest is an ensemble-based anomaly detection method. It isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.
Anomalies are detected based on the anomaly score. The score is calculated using the path length from the root node to the terminating node. A higher score indicates a higher likelihood of the point being an anomaly.
Integrate more data sources, including other major exchanges (e.g., Kraken, Coinbase) and blockchain data providers. This diversity can improve the robustness of anomaly detection.
Combine the isolation forest with other machine learning models such as LSTM (Long Short-Term Memory networks) for time series anomaly detection. This can help capture temporal dependencies in the data.
Use an ensemble of models to improve detection accuracy. For example, combining isolation forest, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and one-class SVM (Support Vector Machine).
Enhance feature engineering by including more sophisticated features such as trading volumes, order book depth, price volatility, and trade frequency.
Implement parallel processing and concurrency in data fetching and processing to handle higher volumes of data and reduce latency.
Utilize stream processing frameworks like Apache Kafka and Apache Flink to handle real-time data ingestion and processing more efficiently.
Refactor the system into a microservices architecture to improve scalability and maintainability. Each component (data fetching, anomaly detection, reporting) can run as an independent service.
Use Docker for containerization and Kubernetes for orchestration to ensure seamless deployment and scaling of the system across different environments.
Implement real-time alerting mechanisms using messaging platforms like Sentry, Slack, or email notifications for immediate action on detected anomalies.
Develop a web-based dashboard to visualize real-time data, anomalies, and statistical reports. Tools like Grafana or Kibana can be useful for this purpose.
Ensure that data fetching, storage, and processing comply with best security practices, including encryption and secure API usage.
Ensure the system adheres to relevant financial regulations and standards, particularly those related to fraud detection and anti-money laundering (AML).
Implement mechanisms for continuous model retraining and adaptation to evolving market conditions and fraud tactics.
Incorporate a feedback loop where detected anomalies can be reviewed and labeled by experts, improving the training data and model accuracy over time.
This project uses data from Binance and CoinGecko APIs.