Systems and methods for machine learning using adversarial learning and data transformation

Abstract

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.