Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

Musleh, Ahmed S.; Debouza, Mahdi; Khalid, Haris M.; Al‐Durra, Ahmed

doi:10.1109/iecon.2019.8927453

Cited by 11 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A, B, and C feed compositions (stream 4) Random variation IDV (9) D feed temperature (stream 2) Random variation IDV (10) C feed temperature (stream 4) Random variation IDV (11) Reactor cooling water inlet temperature Random variation IDV (12) Condenser cooling water inlet temperature Random variation IDV (13) Reaction kinetics Slow shift IDV (14) Reactor cooling water valve Sticking IDV (15) Condenser cooling water valve Sticking IDV (16) Unknown Unknown IDV (17) Unknown Unknown IDV (18) Unknown Unknown IDV (19) Unknown Unknown IDV (20) Unknown Unknown IDV (21) Valve position constant (stream 4) Constant position…”

Section: Process Variable Typementioning

confidence: 99%

“…PCA is a powerful tool for dimension reduction, which allows the most important variable information to be retained. It has been extensively used in feature extraction, data compression, image processing, and pattern recognition [8,9]. ICA can process high-intensity, high-noise and related data by extracting independent statistical variables hidden in the process to achieve process dimensionality reduction [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Zhang

Luo

et al. 2021

Sensors

View full text Add to dashboard Cite

Fault detection and diagnosis (FDD) has received considerable attention with the advent of big data. Many data-driven FDD procedures have been proposed, but most of them may not be accurate when data missing occurs. Therefore, this paper proposes an improved random forest (RF) based on decision paths, named DPRF, utilizing correction coefficients to compensate for the influence of incomplete data. In this DPRF model, intact training samples are firstly used to grow all the decision trees in the RF. Then, for each test sample that possibly contains missing values, the decision paths and the corresponding nodes importance scores are obtained, so that for each tree in the RF, the reliability score for the sample can be inferred. Thus, the prediction results of each decision tree for the sample will be assigned to certain reliability scores. The final prediction result is obtained according to the majority voting law, combining both the predicting results and the corresponding reliability scores. To prove the feasibility and effectiveness of the proposed method, the Tennessee Eastman (TE) process is tested. Compared with other FDD methods, the proposed DPRF model shows better performance on incomplete data.

show abstract

Section: Process Variable Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Zhang

Luo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…With the rapid developments of artificial intelligence technologies, the research works of data-driven technologybased detection methods are increasing dramatically. e principle component analysis is used to analyze the FDI attacks in the real-time environment [17], providing a more accurate and sensitive response than the previous FDI detection techniques. In [18], a supervised learning using labeled data called support vector machine-based FDI attacks detection method is proposed.…”

Section: Introductionmentioning

confidence: 99%

Stacked Autoencoder Framework of False Data Injection Attack Detection in Smart Grid

Chen

Gu²,

Wang

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The advanced communication technology provides new monitoring and control strategies for smart grids. However, the application of information technology also increases the risk of malicious attacks. False data injection (FDI) is one kind of cyber attacks, which cannot be detected by bad data detection in state estimation. In this paper, a data-driven FDI attack detection framework of the smart grid with phasor measurement units (PMUs) is proposed. To enhance the detecting accuracy and efficiency, the multiple layer autoencoder algorithm is applied to abstract the hidden features of PMU measurements layer by layer in an unsupervised manner. Then, the features of the measurements and corresponding labels are taken as inputs to learn a softmax layer. Last, the autoencoder and softmax layer are stacked to form a FDI detection framework. The proposed method is applied on the IEEE 39-bus system, and the simulation results show that the FDI attacks can be detected with higher accuracy and computational efficiency compared with other artificial intelligence algorithms.

show abstract

“…The DA is proposed because traditional FDIAs can be easily detected using the clustering-based and principal component analysis (PCA)-based approaches [9], [10]. The malicious measurements of the DA are deeply hidden in normal measurements because the distances between them and normal ones are minimized.…”

mentioning

confidence: 99%

Letter Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

Zhang

Qin

Wang

et al. 2023

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

Detection of False Data Injection Attacks in Smart Grids: A Real-Time Principle Component Analysis

Cited by 11 publications

References 19 publications

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data

Stacked Autoencoder Framework of False Data Injection Attack Detection in Smart Grid

Letter Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

Contact Info

Product

Resources

About