FraudFox: 实际世界中可适应的欺诈检测
FraudFox: Adaptable Fraud Detection in the Real World
AI总结：
FraudFox通过动态重要权重的扩展卡尔曼滤波器解决资源受限环境下的对抗攻击问题，提出最优决策面和帕累托最优集以应对欺诈行为变化，已应用于亚马逊生产环境。
摘要：
所提出的方法（FraudFox）为资源受限环境中对抗攻击提供了解决方案。我们关注诸如：Smith在周一凌晨3点试图购买500美元鞋子是否可疑？如何在对抗环境中将多个风险评估模块（或acles）的风险评分合并？更重要的是，给定历史数据（订单、价格及后续事件）和业务目标/限制，哪些交易（如Smith交易）应被通过， versus 发送给人工调查？业务限制可能为：最多可行x次调查，或最多因欺诈损失y美元。这两个研究问题是我们工作的重点。解决第一个问题（oracle-weighting）的方法是使用具有动态重要权重的扩展卡尔曼滤波器，以自动且连续地更新每个'oracle'的权重。对于第二个问题，我们展示了如何推导最优决策面以及如何计算帕累托最优集，以允许what-if问题。重要考虑是适应性：欺诈者会根据我们的过去决定改变行为；因此，我们需要相应适应。所得到的系统（方法）是可扩展的，能够适应欺诈者行为的变化，有效，并已应用于亚马逊生产环境。FraudFox增强了欺诈预防子系统，并带来了显著的性能提升。
英文摘要：
The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the following: How suspicious is `Smith', trying to buy \$500 shoes, on Monday 3am? How to merge the risk scores, from a handful of risk-assessment modules (`oracles') in an adversarial environment? More importantly, given historical data (orders, prices, and what-happened afterwards), and business goals/restrictions, which transactions, like the `Smith' transaction above, which ones should we `pass', versus send to human investigators? The business restrictions could be: `at most $x$ investigations are feasible', or `at most \$$y$ lost due to fraud'. These are the two research problems we focus on, in this work. One approach to address the first problem (`oracle-weighting'), is by using Extended Kalman Filters with dynamic importance weights, to automatically and continuously update our weights for each 'oracle'. For the second problem, we show how to derive an optimal decision surface, and how to compute the Pareto optimal set, to allow what-if questions. An important consideration is adaptation: Fraudsters will change their behavior, according to our past decisions; thus, we need to adapt accordingly. The resulting system, \method, is scalable, adaptable to changing fraudster behavior, effective, and already in \textbf{production} at Amazon. FraudFox augments a fraud prevention sub-system and has led to significant performance gains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
