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，了解多元时间序列的相互作用至关重要。具有注意机制的图形神经网络（GNN）已被证明有效地学习和预测了这种时间序列。该项目旨在调查GNNS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。该项目将提供培训机会并促进代表性不足的学生的STEM教育。该项目的目的是应对多变量时间序列中威胁检测的三个关键挑战：1）维持深度GNN的准确性，2）培训GNN的数据有限，并且3）在培训中降低培训中的计算成本，并在培训中降低了深入的GNN，并与注意层进行了注意。该研究基于偏微分方程（PDE）理论的连续深度GNN和有效的注意算法。通过利用GNN的持续观点，该项目旨在开发理论上和计算有效的算法，以通过有限的监督进行准确的图形深度学习。该项目将重点放在三个研究推力上：推力A：桥接扩散方程理论和GNN体系结构设计，以基于图上的扩散方程开发新的GNN类。这些GNN在有限的监督下克服了过度光滑的，并可靠地学习和预测。推力B：开发用于GNN和注意力训练，测试和推理的快速算法。推力C：新算法在异常检测和软件开发中的应用，特别是在基准图表学习任务和交通流，电源分配和流行数据中的异常检测中。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来通过评估来通过评估来评估的。