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

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 tothis http URLmitigate 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.

@article{luo2025_2507.02635,
  title={ SAT-BO: Verification Rule Learning and Optimization for FraudTransaction Detection },
  author={ Mao Luo and Zhi Wang and Yiwen Huang and Qingyun Zhang and Zhouxing Su and Zhipeng Lv and Wen Hu and Jianguo Li },
  journal={arXiv preprint arXiv:2507.02635},
  year={ 2025 }
}