Adversarial Retraining for Fraud Detection Model Robustness
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Solution Overview
Problem
Machine learning-based fraud detection systems become obsolete due to sophisticated adversarial attacks that exploit vulnerabilities in classification models, leading to incorrect classifications and security vulnerabilities.
Innovation Solution
The system generates an ensemble attack generator model to create sparse perturbed training examples, which are used to retrain the machine learning model, adjusting weights to minimize the impact of adversarial attacks by identifying and mitigating vulnerabilities through adversarial retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning models are used for fraud detection, then the system can process transactions efficiently, but the models become obsolete due to sophisticated adversarial attacks
Solution Approach 1:
The system performs preliminary actions by generating adversarial examples and creating perturbed training data before the actual fraud detection task. This pre-processing step prepares the model to withstand future attacks by exposing it to potential attack vectors during training, thereby improving robustness without sacrificing processing efficiency during deployment
Solution Approach 2:
The system applies preliminary anti-action by using adversarial examples to train the model against potential attacks before deployment. The perturbed training data incorporates adversarial perturbations that counteract future adversarial attacks, creating a defensive mechanism that protects the model while maintaining its original processing capabilities
2Reliability
If the machine learning model is retrained with perturbed training examples to improve robustness, then susceptibility to adversarial attacks is reduced, but training time and computational resources increase
Solution Approach 1:
The system applies partial action by generating perturbed training examples for only a subset of the training data rather than completely retraining on all data. Adversarial perturbations are applied selectively to portions of the training set, achieving robustness improvements while reducing the computational burden and time required for retraining
Solution Approach 2:
The system changes parameters by modifying the training data through adversarial perturbations rather than fundamentally changing the model architecture or training algorithm. This approach improves robustness by altering the input data distribution while keeping the training process itself relatively efficient, avoiding the need for extensive retraining
3Measurement precision
If adversarial examples are generated to improve model robustness, then accuracy in identifying fraudulent transactions is improved, but the complexity of the training process increases
Solution Approach 1:
The system uses copying by generating perturbed versions of existing training examples rather than creating entirely new training data or complex adversarial scenarios. This approach simplifies the training process by reusing existing data structures and formats while incorporating adversarial perturbations, thereby improving detection accuracy without significantly increasing training complexity
Data Source
AI summary
Techniques are disclosed for automatically retraining a machine learning model based on the performance of this model falling below a performance threshold. In some embodiments, a computer system compares output of a new machine learning model for a new set of examples with known labels for examples in the new set of examples, wherein the new set of examples includes one or more new features. In some embodiments, the computer system determines, based on the comparing, whether a current performance of the new machine learning model satisfies a performance threshold for machine learning models, where the performance threshold is based on output of a benchmark machine learning model. In some embodiments, the computer system automatically triggers, in response to determining that the current performance of the new model does not satisfy the performance threshold, retraining of the new model.