AI 中文总结
本文针对网络物理系统攻击检测中现有图注意力模型延迟过高的问题，提出GraphGHHA图原生注意力加速层，在保持检测精度的同时将检测延迟最高降低8倍，实现了大规模CPS监测的实时警报生成。
AI 中文摘要
网络物理系统（CPS）由传感器、控制器和执行器通过通信与物理交互构成，易受到针对测量值、控制逻辑及设备运行的攻击。基于图的攻击检测器（尤其是图注意力模型）可通过学习通信与物理交互图上的边自适应交互来定位此类攻击，但计算成本会随邻域规模快速增长，在大规模或密集连接图中推理延迟可达数十至数百毫秒，导致时间关键型警报生成延迟。为实现用于攻击检测的图注意力机制的实时部署，本文提出GraphGHHA，一种图原生注意力加速层，旨在替换攻击检测单元中的图注意力层。GraphGHHA结合了（i）受CPS邻接矩阵约束的图局部稀疏注意力分支，以及（ii）全局线性混合分支以保留系统级信息；可学习门按节点级组合两个分支，在严格延迟约束下保持检测精度。我们在网络化暖通空调（HVAC）网络物理系统的代表性攻击场景下评估GraphGHHA，结果显示其检测延迟最高降低8倍，同时保持高检测精度，表明GraphGHHA可实现大规模CPS监测的实用型实时警报生成。
英文摘要
Cyber-physical systems (CPSs) consist of sensors, controllers, and actuators through communication and physical interactions, making them vulnerable to attacks on measurements, control logic, and equipment operation. Graph-based attack detectors, especially graph attention models, can localize such attacks by learning edge-adaptive interactions over communication and physical interaction graphs. However, their computational cost grows rapidly with neighborhood size, and their inference latency can reach tens to hundreds of milliseconds in large-scale or densely connected graphs, delaying time-critical alarm generation. To enable real-time deployment of graph attention mechanisms for attack detection, we propose GraphGHHA, a graph-native attention acceleration layer designed as a replacement for graph attention layers in attack detection units. GraphGHHA combines (i) a graph-local sparse attention branch that is constrained by the adjacency matrix of the CPS, and (ii) a global linear mixing branch to retain system-wide information. A learnable gate combines the two branches node-wise, preserving detection accuracy under strict latency constraints. We evaluate GraphGHHA on a networked heating, ventilation, and air conditioning (HVAC) cyber-physical system under representative attack scenarios and demonstrate up to an eight-fold reduction in detection latency while maintaining high detection accuracy. These results indicate that GraphGHHA enables practical, real-time alarm generation for large-scale CPS monitoring.
