Generating Adversarial Examples & Defense Methods For Online Fraud Detection

With the temporally evolving and diverse nature of fraud patterns in online payment systems, optimizing explicitly for recognition and defense against such attacks has assumed greater precedence. This talk introduces a cohort of domain-oriented adversarial attacks based on poisoning and evasion techniques. We further demonstrate the effectiveness of such cohorts in surpassing the attack capabilities of isolated attack frameworks. To proactively neutralize ensemble-oriented attacks, defensive procedures are demonstrated. Lastly, key challenges and applications in effective deployment of such models in large-scale fraud detection systems to identify temporally evolving attacks will be broadly addressed.