Collaborative Research: ATD: Fast Algorithms and Novel Continuous-depth Graph Neural Networks for Threat Detection

合作研究：ATD：用于威胁检测的快速算法和新颖的连续深度图神经网络

• 批准号: 2219956
• 负责人: Bao Wang
• 金额: $ 12.5万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219956&HistoricalAwards=false
• 关键词: Collaborative; Research; ATD; Fast; Algorithms

In algorithmic threat detection, understanding the interactions of multivariate time series is crucial. Graph neural networks (GNNs) with attention mechanisms have proven effective in learning and predicting such time series. This project aims to investigate GNNs for improved acceleration and accuracy. The research will have broad applicability in fields such as Artificial Intelligence (AI), traffic analysis, power systems, and health analytics. The project will provide training opportunities and promote STEM education for underrepresented students.The project aims to address three key challenges in threat detection within multivariate time series: 1) maintaining accuracy with deep GNNs, 2) training GNNs with limited data, and 3) reducing computational costs in training and deploying deep GNNs with attention layers. The research advances continuous-depth GNNs and efficient attention algorithms based on the partial differential equation (PDE) theory. By leveraging the continuous viewpoint of GNNs, the project aims to develop theoretically-grounded and computationally efficient algorithms for accurate graph deep learning with limited supervision. The project will focus on three research thrusts: Thrust A: Bridging diffusion equation theory and GNN architecture design to develop a new class of GNNs based on diffusion equations on graphs. These GNNs overcome over-smoothing and reliably learn and predict with limited supervision. Thrust B: Developing fast algorithms for GNN and attention training, testing, and inference. Thrust C: Application of the new algorithms to anomaly detection and software development, specifically in benchmark graph learning tasks and anomaly detection in traffic flow, power distribution, and epidemic data.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.

在算法威胁检测中，理解多元时间序列的相互作用至关重要。事实证明，具有注意力机制的图神经网络（GNN）在学习和预测此类时间序列方面是有效的。该项目旨在研究 GNN 以提高加速度和准确性。该研究将在人工智能（AI）、交通分析、电力系统和健康分析等领域具有广泛的适用性。该项目将为代表性不足的学生提供培训机会并促进 STEM 教育。该项目旨在解决多变量时间序列内威胁检测的三个关键挑战：1）保持深度 GNN 的准确性，2）用有限的数据训练 GNN，3）减少训练和部署具有注意力层的深度 GNN 的计算成本。该研究提出了基于偏微分方程（PDE）理论的连续深度 GNN 和高效注意力算法。通过利用 GNN 的连续观点，该项目旨在开发有理论基础且计算高效的算法，用于在有限监督下进行精确的图深度学习。该项目将重点关注三个研究重点： 重点 A：桥接扩散方程理论和 GNN 架构设计，开发一类基于图扩散方程的新型 GNN。这些 GNN 克服了过度平滑的问题，并在有限的监督下可靠地学习和预测。主旨 B：开发 GNN 和注意力训练、测试和推理的快速算法。主旨 C：将新算法应用于异常检测和软件开发，特别是基准图学习任务以及交通流、电力分配和流行病数据中的异常检测。该奖项反映了 NSF 的法定使命，经评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。