Work

Multi scale attention driven DACDiff+ distributed power FDIAs defense model

Year: 2026

Type: article

Abstract: This paper proposes a novel defense model called DACDiff + to address false data injection attacks (FDIAs) in distributed power systems. This model integrates multi-scale attention mechanism and dynamic adaptive diffusion model, which can effectively capture short, medium, and long-term dependencies in time series data and dynamically adjust the denoising step size according to the attack intensity. The experimental results show that DACDiff+improves data recovery accuracy by 32.7% compared to traditional statistical methods, achieves a defense success rate of 86.7% against stealth attacks, and has a inference time of 32.5ms, meeting real-time requirements. The model validated the key role of multi-scale attention mechanism and dynamic diffusion model through ablation experiments, providing an efficient solution for smart grid security.

Source: Discover Artificial Intelligence

DOI: 10.1007/s44163-026-01633-1

Authors: Jie Wang, Chang Liu, Guowei Zhu, Jiangpei Xu, Feng Long

Institutions: State Grid Corporation of China (China), State Grid Hubei Electric Power Company Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd. (China)

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,590