Adversarial Learning Framework for Fraud Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models are vulnerable to cyberattacks due to data imbalance and adversarial attacks, which can lead to cybersecurity vulnerabilities, especially in credit card fraud detection, where imbalanced datasets and adversarial manipulations of feature distributions evade detection, causing erroneous outputs and compromising the security of enterprise computing infrastructure.
Innovation Solution
The proposed solution involves a Negative-Calibrated Generative Adversarial Network (NCGAN) and an Adversarial Learning with Sum of Top-K loss (AST) framework, which generates synthetic data to balance imbalanced datasets and adapts the model to counter adversarial attacks by introducing a penalty term and focusing on critical samples, respectively, to enhance the robustness of machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional machine learning models are used for fraud detection, then the system can process large amounts of data without explicit programming, but the models become vulnerable to adversarial attacks and data imbalance issues
Solution Approach 1:
The system performs preliminary hardening of machine learning models by generating adversarial examples and augmenting training data before deployment. This proactive measure prepares the model to resist future attacks by pre-exposing it to manipulated inputs and balanced data distributions, thereby improving reliability while maintaining automation.
Solution Approach 2:
The system applies preliminary anti-action by intentionally creating adversarial examples and using them to train the model before actual attacks occur. This preemptive countermeasure builds resistance against future adversarial attacks, addressing the vulnerability to manipulation while preserving automated detection capabilities.
2Ease of manufacture
If machine learning models are trained on imbalanced datasets, then the system can learn from available data, but the models become biased and fail to detect minority class fraud cases effectively
Solution Approach 1:
The system creates synthetic copies of minority class fraud samples by generating adversarial examples and using data augmentation techniques. These synthetic copies balance the training dataset, allowing the model to learn fraud patterns effectively without requiring additional real fraud data, thereby improving detection accuracy while maintaining ease of training data availability.
Solution Approach 2:
The system changes the parameter distribution of training data by applying data augmentation transformations and balancing techniques to imbalanced datasets. This modifies the data distribution to give appropriate weight to minority class samples, improving measurement precision in fraud detection while working with the originally available data.
3Adaptability or versatility
If fraudsters manipulate feature distributions to evade detection, then they can maximize their gains, but this creates a dynamic adversarial scenario that requires continuous model adaptation
Solution Approach 1:
The system implements dynamic adaptability by integrating continuous model hardening processes that regularly update the model with newly generated adversarial examples. This creates a dynamic defense system that evolves alongside attacker strategies, maintaining effectiveness against changing threats while managing complexity through automated incremental updates rather than complete retraining.
Data Source
AI summary
A machine learning hardening system and method for hardening machine learning models against adversarial attacks are disclosed, addressing the prevalent challenges of data imbalance and sophisticated adversarial attacks. The method involves preprocessing data for logistic regression, and augmenting the dataset with synthetic fraudulent transactions generated by a Negative-Calibrated Generative Adversarial Network (NCGAN) to ensure balance. Adversarial behavior is modeled to simulate fraudster responses to detection models, employing a Stackelberg game framework. A logistic regression model is trained using a sum of top-K loss function to focus on the transactions with the greatest loss, enhancing the detection of fraudulent activity.