SAT-BO: Verification Rule Learning and Optimization for FraudTransaction Detection

TL;DR

This paper introduces SAT-BO, a method for verifying, learning, and optimizing rules to detect and prevent fraudulent transactions in electronic payment systems, aiming to enhance security and robustness.

Contribution

It presents a systematic approach combining verification, rule learning, and optimization to improve the effectiveness of fraud detection rules.

Findings

Successfully identifies vulnerabilities in verification rules.

Improves detection accuracy through optimized rule learning.

Reduces false positives in transaction validation.

Abstract

Electronic payment platforms are estimated to process billions oftransactions daily, with the cumulative value of these transactionspotentially reaching into the trillions. Even a minor error within thishigh-volume environment could precipitate substantial financiallosses. To mitigate this risk, manually constructed verification rules,developed by domain experts, are typically employed to identifyand scrutinize transactions in production environments. However,due to the absence of a systematic approach to ensure the robust-ness of these verification rules against vulnerabilities, they remainsusceptible to exploitation.To mitigate this risk, manually constructed verification rules, de-veloped by domain experts, are typically employed to identify andscrutinize transactions in production environments. However, dueto the absence of a systematic approach to ensure the robustness ofthese verification rules against vulnerabilities, they remain suscep-tible to exploitation. To ensure data security, database maintainersusually compose complex verification rules to check whether aquery/update request is valid. However, the rules written by ex-perts are usually imperfect, and malicious requests may bypassthese rules. As a result, the demand for identifying the defects ofthe rules systematically emerges.