用张量分解与推荐模型结合，提升电网攻击检测效率。
Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model
- 引入张量列车分解压缩嵌入，降低计算开销。
- 在真实电网数据集上实现更高吞吐，检测延迟显著下降。
- 兼容PyTorch，适合部署在资源受限的边缘设备。
深度学习模型因其捕捉非结构化稀疏特征的能力，被广泛用于智能电网中的虚假数据注入攻击（FDIA）检测。然而，系统规模和数据维度的增长带来了巨大的计算与内存负担，尤其在大规模工业数据集上，严重制约了检测效率。为此，本文提出Rec-AD框架，将张量列车分解与深度学习推荐模型（DLRM）相结合。该框架通过嵌入压缩、索引重排优化数据访问，以及流水线训练机制，有效降低内存通信开销，提升训练与推理效率。完全兼容PyTorch，无需代码修改即可集成至现有FDIA检测系统。实验表明，Rec-AD显著提升计算吞吐量与实时检测性能，缩小攻击窗口，提高攻击者成本。该方法增强了边缘计算能力与系统可扩展性，为智能电网安全提供有力技术支撑。
原文摘要 · Abstract (English)
Deep learning models have been widely adopted for False Data Injection Attack (FDIA) detection in smart grids due to their ability to capture unstructured and sparse features. However, the increasing system scale and data dimensionality introduce significant computational and memory burdens, particularly in large-scale industrial datasets, limiting detection efficiency. To address these issues, this paper proposes Rec-AD, a computationally efficient framework that integrates Tensor Train decomposition with the Deep Learning Recommendation Model (DLRM). Rec-AD enhances training and inference efficiency through embedding compression, optimized data access via index reordering, and a pipeline training mechanism that reduces memory communication overhead. Fully compatible with PyTorch, Rec-AD can be integrated into existing FDIA detection systems without code modifications. Experimental results show that Rec-AD significantly improves computational throughput and real-time detection performance, narrowing the attack window and increasing attacker cost. These advancements strengthen edge computing capabilities and scalability, providing robust technical support for smart grid security.
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