CSR: Small: Multi-FPGA System for Real-time Fraud Detection with Large-scale Dynamic Graphs

CSR：小型：利用大规模动态图进行实时欺诈检测的多 FPGA 系统

• 批准号: 2317251
• 负责人: Cong Hao
• 金额: $ 55.47万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317251&HistoricalAwards=false
• 关键词: CSR; Small; Multi; FPGA; System

The rise of international financial and cybercrime, such as fraud and money laundering, has led to billions of dollars in losses annually in the United States. To address this urgent issue, there is a need for real-time fraud detection algorithms and systems with extremely low latency. Graph-based machine learning algorithms, specifically Graph Neural Networks (GNNs), have emerged as a promising solution. Financial activity graphs possess two crucial characteristics: they are extremely large, comprising vast amounts of transactions, financial institutions, and customers, and they evolve dynamically over time as new transactions occur. These characteristics present significant challenges for real-time, low-latency fraud detection, as it requires a scalable and parallelizable distributed system. Additionally, the dynamic nature of the graphs poses challenges for system control and optimization, as the partitioning of the problem on a distributed system may quickly become suboptimal due to graph updates. To tackle these challenges, this project proposes a distributed FPGA (Field-Programmable Gate Array) system for real-time fraud detection on large-scale dynamic graphs. The project aims to achieve microsecond-level latency, which has not been explored in real-time distributed dynamic GNNs. The research involves three main tasks: system construction, dynamic optimization, and uncertainty analysis and optimization. The impacts of this project are significant. Successful implementation will enhance the effectiveness of fraudulent transaction alerts for millions of people globally and help businesses reduce fraud losses and increase revenue. The project's outcomes can have applications in various domains, such as cybercrime detection, insurance fraud, national security infrastructure protection, and identifying anomalies and terrorist attacks. The constructed system will be made publicly accessible, and all codes will be open-sourced to benefit the community. Furthermore, the project presents an opportunity to involve students, including those from underrepresented groups, in research and education through collaborations and engaging competitions.The project aims to address the rising challenges of international financial and cybercrime by developing a distributed FPGA system for real-time fraud detection on large-scale dynamic graphs. Current fraud detection systems lack low-latency capabilities, making real-time detection difficult. To overcome this, the project proposes the use of Graph Neural Networks (GNNs) and introduces three key tasks: system construction, dynamic optimization, and uncertainty analysis and optimization. The financial graphs involved in fraud detection are characterized by their immense size and dynamic nature. These attributes pose significant obstacles to real-time detection and system optimization. To tackle these challenges, we plan to utilize a distributed FPGA system that can handle large-scale dynamic graphs efficiently. By leveraging Smart Network Interface Cards (SmartNICs) and multi-agent reinforcement learning (MARL), the system will dynamically repartition evolving graphs across FPGAs for optimal performance. Additionally, we propose to use Bayesian Neural Networks (BNNs) to model and analyze system predictability and uncertainty. This information is crucial for real-time systems. The BNN will guide active learning strategies, allowing the system to make informed decisions when faced with high uncertainty.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

国际金融和网络犯罪（例如欺诈和洗钱）的兴起导致美国每年造成数十亿美元的损失。为了解决这个紧急问题，需要具有极低延迟的实时欺诈检测算法和系统。基于图形的机器学习算法，特别是图形神经网络（GNN），已成为有前途的解决方案。财务活动图具有两个关键特征：它们非常大，包括大量交易，金融机构和客户，随着新交易的发生，它们会随着时间的流逝而动态发展。这些特征对实时，低延迟欺诈检测提出了重大挑战，因为它需要可扩展且可行的分布式系统。此外，图形的动态性质对系统控制和优化构成了挑战，因为由于图形更新，分布式系统上问题的分配可能会迅速降低。为了应对这些挑战，该项目提出了一个分布式FPGA（现场编程的门阵列）系统，用于在大规模动态图上进行实时欺诈检测。该项目旨在达到微秒级别的延迟，尚未在实时分布式动态GNN中探索。该研究涉及三个主要任务：系统构建，动态优化以及不确定性分析和优化。该项目的影响很大。成功的实施将提高全球数百万人的欺诈交易警报的有效性，并帮助企业减少欺诈损失并增加收入。该项目的结果可以在各个领域中提供应用程序，例如网络犯罪检测，保险欺诈，国家安全基础设施保护，并确定异常和恐怖袭击。构造的系统将被公开访问，所有代码都将被开源以使社区受益。此外，该项目为学生提供了一个机会，包括通过合作和参与竞争的学生参与研究和教育的学生。该项目旨在通过开发分布式的FPGA系统来解决在大型动态图上实时欺诈检测的分布式FPGA系统的不断增加的挑战。当前的欺诈检测系统缺乏低延迟功能，因此难以实时检测。为了克服这一点，该项目提出了图形神经网络（GNN）的使用，并介绍了三个关键任务：系统构建，动态优化以及不确定性分析和优化。欺诈检测所涉及的财务图的特征是它们的巨大规模和动态性质。这些属性对实时检测和系统优化构成了重大障碍。为了应对这些挑战，我们计划利用可以有效处理大规模动态图的分布式FPGA系统。通过利用智能网络接口卡（智能网络）和多代理增强学习（MARL），该系统将动态重新分配跨FPGA的图形，以获得最佳性能。此外，我们建议使用贝叶斯神经网络（BNN）来建模和分析系统的可预测性和不确定性。此信息对于实时系统至关重要。 BNN将指导积极的学习策略，使系统在面对高度不确定性时可以做出明智的决策。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来支持的。