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

欺诈和洗钱等国际金融和网络犯罪的增加，每年给美国造成数十亿美元的损失。为了解决这一紧迫问题，需要具有极低延迟的实时欺诈检测算法和系统。基于图的机器学习算法，特别是图神经网络（gnn），已经成为一种很有前途的解决方案。金融活动图具有两个关键特征：它们非常大，包含大量的交易、金融机构和客户，并且随着新交易的发生而动态发展。这些特征对实时、低延迟的欺诈检测提出了重大挑战，因为它需要可扩展和可并行的分布式系统。此外，图的动态特性给系统控制和优化带来了挑战，因为由于图更新，分布式系统上的问题分区可能很快就会变得次优。为了应对这些挑战，本项目提出了一种分布式FPGA（现场可编程门阵列）系统，用于大规模动态图形的实时欺诈检测。该项目旨在实现微秒级延迟，这在实时分布式动态gnn中尚未得到探索。研究主要包括三个方面：系统构建、动态优化和不确定性分析与优化。这个项目的影响是巨大的。成功实施将为全球数百万人提高欺诈性交易警报的有效性，并帮助企业减少欺诈损失并增加收入。该项目的成果可以应用于各种领域，例如网络犯罪检测、保险欺诈、国家安全基础设施保护以及识别异常和恐怖袭击。构建的系统将向公众开放，所有代码都将开源，以使社区受益。此外，该项目提供了一个机会，让学生，包括那些来自代表性不足的群体，通过合作和参与竞赛参与研究和教育。该项目旨在通过开发用于大规模动态图形实时欺诈检测的分布式FPGA系统，解决国际金融和网络犯罪日益严峻的挑战。目前的欺诈检测系统缺乏低延迟能力，使得实时检测变得困难。为了克服这一问题，该项目提出使用图神经网络（gnn），并引入了三个关键任务：系统构建、动态优化和不确定性分析与优化。欺诈检测所涉及的财务图表具有规模巨大、动态多变的特点。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，我们计划利用分布式FPGA系统，可以有效地处理大规模动态图形。通过利用智能网络接口卡（smartnic）和多智能体强化学习（MARL），系统将动态地重新划分fpga上的进化图，以获得最佳性能。此外，我们建议使用贝叶斯神经网络（BNNs）来建模和分析系统的可预测性和不确定性。这些信息对实时系统至关重要。BNN将指导主动学习策略，允许系统在面临高度不确定性时做出明智的决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.