Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning

Niu, Xiangyu; Li, Jiangnan; Sun, Jianhua; Tomsovic, Kevin

doi:10.1109/isgt.2019.8791598

Cited by 90 publications

(45 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recent breakthrough in computing provides the foundation for "deep" neural network. Niu et al [119] developed a smart grid anomaly detection framework based on a neural network. The recurrent neural network with a long short-term memory cell is deployed to capture the dynamic behavior of power systems.…”

Section: Data-driven Approachesmentioning

confidence: 99%

Smart Grid Cyber-Physical Attack and Defense: A Review

2021

View full text Add to dashboard Cite

Recent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of new devices based on the information and communication technology (ICT). However, these ICT-enabled devices are susceptible to a growing threat of cyber-physical attacks. This paper performs a thorough review of the state-of-the-art cyber-physical security of the smart grid. By focusing on the physical layer of the CPSG, this paper provides an abstracted and unified state-space model, in which cyber-physical attack and defense models can be effectively generalized. The existing cyber-physical attacks are categorized in terms of their target components. We then discuss several operational and informational defense approaches that present the current state-of-the-art in the field, including moving target defense, watermarking, and datadriven approaches. Finally, we discuss challenges and future opportunities associated with the smart grid cyber-physical security. INDEX TERMS Cyber-physical power system, cyber-physical security, false data injection, dynamic watermarking, moving target defense.

show abstract

Section: Data-driven Approachesmentioning

confidence: 99%

Smart Grid Cyber-Physical Attack and Defense: A Review

2021

View full text Add to dashboard Cite

show abstract

“…Various techniques for detecting anomalies in smart grids have been studied and discussed, such as KNN [ 26 ], Support Vector Machine (SVM) [ 27 ], and LSTM [ 28 ]. These algorithms also discussed in the area of Internet of Things at the network edge [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

A Multitiered Solution for Anomaly Detection in Edge Computing for Smart Meters

Utomo

Hsiung

2020

Sensors

View full text Add to dashboard Cite

In systems connected to smart grids, smart meters with fast and efficient responses are very helpful in detecting anomalies in realtime. However, sending data with a frequency of a minute or less is not normal with today’s technology because of the bottleneck of the communication network and storage media. Because mitigation cannot be done in realtime, we propose prediction techniques using Deep Neural Network (DNN), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). In addition to these techniques, the prediction timestep is chosen per day and wrapped in sliding windows, and clustering using Kmeans and intersection Kmeans and HDBSCAN is also evaluated. The predictive ability applied here is to predict whether anomalies in electricity usage will occur in the next few weeks. The aim is to give the user time to check their usage and from the utility side, whether it is necessary to prepare a sufficient supply. We also propose the latency reduction to counter higher latency as in the traditional centralized system by adding layer Edge Meter Data Management System (MDMS) and Cloud-MDMS as the inference and training model. Based on the experiments when running in the Raspberry Pi, the best solution is choosing DNN that has the shortest latency 1.25 ms, 159 kB persistent file size, and at 128 timesteps.

show abstract

“…Dynamic detection of false data injection attack [15] was proposed in smart grid by using deep learning. In this approach, a CNN and a LSTM network were adopted that observes both data measurements and network level features for mutually learning system states.…”

Section: Survey On Cyber-attack Detection Schemesmentioning

confidence: 99%