About This Tool

This repository contains python code used to create synthetic data samples of minority class for a financial dataset. It also contains a sample of generated synthetic data.

Primary Use Case

This tool is used to create synthetic samples of fraudulent transactions to balance highly imbalanced financial datasets, improving fraud detection model training. Data scientists and security analysts working with credit card fraud datasets can use this to augment their data and enhance model performance.

Key Features

Generates synthetic minority class data for financial fraud datasets
Uses Wasserstein GAN with Gradient Penalty (WGAN-GP) for data synthesis
Addresses class imbalance in credit card fraud detection datasets
Includes sample generated synthetic data for reference
Adapted from AWS Machine Learning best practices

Insights & Recommendations

The tool is still in progress with ongoing evaluation of synthetic data quality; users should validate synthetic data before use in production. It requires familiarity with GANs and Python environment setup.

Smart Usage Notes

Integrate synthetic data generation into fraud detection model pipelines to improve detection accuracy against evolving attack patterns.Use synthetic minority class data to simulate realistic attack scenarios for red and purple team exercises.Leverage synthetic data to augment training datasets, reducing bias and improving model robustness in anomaly detection.Combine with real-time monitoring tools to validate model performance on synthetic vs. real transaction data.Apply synthetic data generation techniques to other imbalanced security datasets, such as insider threat or malware detection.

OpenSec Atlas

Synthetic-financial-data

About This Tool

Primary Use Case

Key Features

Insights & Recommendations

Smart Usage Notes

Security Capability Profile

Tags

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