14 天前 - … FDIAs may occur in multiple locations in the smart grid, especially in medium to
large … to minimize grid losses effectively. Therefore, this paper designs a deep learning-based …

25 天前 - … This paper aims to show the effect of false data injection attacks on the power dataset
… using machine learning approaches. To demonstrate the effects of a false data injection …

35 天前 - … using Deep Reinforcement Learning (… smart home, Home Area Network (HAN),
and grid layers. Finally, an attack detection framework is proposed to detect False Data Injection …

41 天前 - … for attack recovery in the smart grid through the lens of … so that any undetectable
false data injection cannot cause … enhancing CPSs with machine learning capabilities and …

41 天前 - … of the power grid system. Therefore, the effective and accurate detection of
FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based …

47 天前 - … the first study to employ vertical federated learning in the detection of FDIA within
smart grids. Moreover, an attention-based hybrid deep learning model is developed to extract …

62 天前 - … intelligence and machine learning in both enhancing and challenging cybersecurity
within smart grid … CNN for detecting the false data injection to the smart grid system, the …

62 天前 - … , both within the smart grid itself and between smart grid and other critical infrastructure
… under false data injection attacks, and Chap. 9 introduces a deep learning based attack …

66 天前 - … for smart grids with machine learning techniques [11–14] and deep learning techniques
[… the cybersecurity aspects in smart grids, a study on False Data Injection (FDI) attacks …

72 天前 - … ) based stacked autoencoders for capturing temporal dependencies in smart meter
data … However, existing literature has mainly focused on detecting False Data Injection (FDI) …