Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data
This addresses security vulnerabilities in AI-based fraud detection systems for financial institutions, though it is incremental as it adapts existing methods to a new context.
The paper tackles the problem of adversarial attacks on tabular data, specifically for fraud detection with imbalanced datasets, by modifying state-of-the-art algorithms to achieve a perfect attack success rate and less perceptible adversarial examples.
Guaranteeing the security of transactional systems is a crucial priority of all institutions that process transactions, in order to protect their businesses against cyberattacks and fraudulent attempts. Adversarial attacks are novel techniques that, other than being proven to be effective to fool image classification models, can also be applied to tabular data. Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the Artificial Intelligence (AI) system to return incorrect outputs that are advantageous for the attacker. In this paper we illustrate a novel approach to modify and adapt state-of-the-art algorithms to imbalanced tabular data, in the context of fraud detection. Experimental results show that the proposed modifications lead to a perfect attack success rate, obtaining adversarial examples that are also less perceptible when analyzed by humans. Moreover, when applied to a real-world production system, the proposed techniques shows the possibility of posing a serious threat to the robustness of advanced AI-based fraud detection procedures.