Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model

2025-07-19

AI Summary

To address the excessive computational and memory overhead of False Data Injection Attack (FDIA) detection in large-scale, high-dimensional smart grid scenarios, this paper proposes Rec-AD, an efficient and lightweight detection framework. Methodologically, Rec-AD integrates three key innovations: (1) it introduces Tensor Train (TT) decomposition—the first such application in FDIA detection—to achieve high-fidelity compression of embedding layers; (2) it incorporates the Deep Learning Recommendation Model (DLRM) architecture to enhance sparse feature representation; and (3) it designs index reordering and pipelined training to significantly reduce memory access latency and inter-node communication overhead. Implemented in PyTorch, Rec-AD ensures compatibility with existing deployment infrastructures. Experimental results demonstrate that Rec-AD maintains high detection accuracy (AUC > 0.98) while achieving a 3.2× improvement in inference throughput and a 67% reduction in memory footprint—enabling real-time, edge-deployable FDIA detection and enhancing system scalability.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Detects False Data Injection Attacks in smart grids efficiently
• Reduces computational and memory burdens in large-scale datasets
• Enhances training and inference efficiency with embedding compression

Innovation

• Tensor Train decomposition for embedding compression
• Index reordering optimizes data access
• Pipeline training reduces memory overhead