A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids

Wang, Yi; Amin, Mahmoud M.; Fu, Jianlong; Moussa, Heba B.

doi:10.1109/access.2017.2769099

Cited by 132 publications

(56 citation statements)

References 42 publications

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“…This change in measurement is undetected by the current state estimation techniques [65]. Furthermore, these attacks can be created in various strategies with limited knowledge of power system topology [66,67,68]. As such, these types of attacks are widely studied in the smart grid cybersecurity field [63,65,66,67,68,69,70,71,72,73,74,75].…”

Section: False Data Injection Attacksmentioning

confidence: 99%

“…The results of comparing these learning algorithms demonstrate that a Gaussian-based Support Vector Machine (SVM) is more robust with more accurate classification among larger test systems [95]. Furthermore, another paper implemented the margin setting algorithm (MSA) demonstrating better results than SVM and artificial neural networks (ANN) [96,25]. Other intelligent techniques include adaboost, random forests, and common path mining method [97,98,99].…”

Section: Detection Of Attacksmentioning

confidence: 99%

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Security aspects of Internet of Things aided smart grids: A bibliometric survey

et al. 2021

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The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.

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Section: False Data Injection Attacksmentioning

confidence: 99%

Section: Detection Of Attacksmentioning

confidence: 99%

Security aspects of Internet of Things aided smart grids: A bibliometric survey

et al. 2021

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“…In [21], the authors recommended a novel data analysis method for detecting false data injection attack mitigation (FDIA) based on a data-centric model using the margin setting algorithm. The performance of the suggested methodology is presented employing the six-bus power network in a measurement system of the wide-area environment through simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks

Chandel¹,

Thakur²

2019

Adv. sci. technol. eng. syst. j.

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The major problem in electric utility is Electrical Theft, which is harmful to electric power suppliers and causes economic loss. Detecting and controlling electrical theft is a challenging task that involves several aspects like economic, social, regional, managerial, political, infrastructural, literacy rate, etc. Numerous methods were proposed formerly for detecting electricity theft. However, the previous works considered only the one dimensional (1-D) energy consumption data which apprehended the periodicity and were found inefficient in storing and retrieving the memory that resulted in a lower accuracy rate of detection. Hence, this research study intends Convolutional Neural Network combined with Bidirectional Long Short Term Memory based Recurrent Neural Network (CNN-RNN-BiLSTM) for overcoming the aforementioned problems in the detection of electricity theft. The CNN captures the global variables of 1-D data and identifies the non-periodicity and periodicity of 2-D electricity consumption records. RNN-BiLSTM extends the memory storage capacity of the neural network with bidirectional flow of information, thereby allowing learning order dependencies. The proposed method results of the predicted values of the electricity theft show better accuracy rate with reduced time during the training phase and reduced number of epochs. The proposed model helps to discriminate the customers for preventing fraudulent activities in the usage of electric power. The validation of the proposed method is carried out by comparing the method with the existing Support Vector Machine (SVM) and multi-class SVM models. The comparative results prove that the proposed CNN-RNN-BiLSTM model of electricity theft detection works efficiently.

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“…It has been applied in many fields, including segmentation analysis in hyperspectral images and impulse noise removal in color images [18][19][20][21]. Recently, it has been applied for anomaly detection for false data injection attack in smart grids and human activity learning [22,23]. However, MSA still leaves a room for improvement in computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

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In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

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A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids

Cited by 132 publications

References 42 publications

Security aspects of Internet of Things aided smart grids: A bibliometric survey

Security aspects of Internet of Things aided smart grids: A bibliometric survey

Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks

A novel parallel learning algorithm for pattern classification

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