Dynamic State Estimation of Generators Under Cyber Attacks

Yang, Li; Li, Zhi; Liang, Chao

doi:10.1109/access.2019.2939055

Cited by 35 publications

(44 citation statements)

References 36 publications

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“…On the other hand, DoS attacks include measurement (packet) loss with two main types of modeling, including Bernoulli distribution [40] and Markov model [41]. The attacker usually initiates DoS attacks by manipulating sensor readings and jamming communication channels, thereby flooding packets in the network [42]. This is illustrated below: where µ k ∈ R is the measurement vector state matrix and µ k (i) denotes the element i in the state transmission matrix.…”

Section: B Adversary Modelmentioning

confidence: 99%

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

2020

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The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support information technology systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false-positive when being put to use. In this paper, we propose a deep learning model to construct new balanced representations of the imbalanced datasets. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold crossvalidation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum effort. INDEX TERMS Cyber-attacks, critical infrastructure, industrial control system, integrity attack, operation technology, information technology, deep learning, neural network.

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Section: B Adversary Modelmentioning

confidence: 99%

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

2020

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“…First, the channel noise of one terminal is analysed. The noise power model of the channel is shown in Formula (3).…”

Section: D2d Communication Channel Model Of Intelligent Terminalmentioning

confidence: 99%

“…However, the current optimisation of electric power communication networks mainly focuses on improving the reliability of cyber-physical systems or enhancing the quality of service (QoS) in networks. For example, Li et al [3] estimated the dynamic state of generators under cyberattacks, in which attacks mainly include false data injection and denial of service attacks in networks. Qu et al [4] proposed a novel method to improve the prediction of power cyber-physical systems risk, where the method of the dependent Markov chain theory was employed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influence of device‐to‐device communication on the access terminal of electric power data communication network

Cui

Liu

Sun

et al. 2021

J. eng.

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The expansion of the electric power communication network year by year has enabled a large number of intelligent terminals to be accessed exponentially. Massive intelligent terminals connected to the power communication network have a huge processing demand, which causes a heavy maintenance burden on the equipment of communication networks. In order to solve this problem, this study proposes a flexible communication mechanism based on the device-to-device (D2D) technique for the intelligent terminals to deal with the processing pressure of massive information in the network. This proposed mechanism introduces where and how to use the D2D technique. In this study, this communication mechanism makes terminals give a high priority to transferring the information of important services. The D2D communication model is formulated for this communication mechanism, and the influence of different conditions on the D2D communication is analysed. The D2D communication mechanism is simulated to optimise the existing network indexes, such as traffic and sum of service level. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Therefore, the power system is facing serious threats of network attacks continuously. Different from the network attacks in the Internet field, cyber-attacks against CPPS concentrate more on destroying the stability control of the cyber layer over the physical layer, even paralyze the operation of the power system [3]. The blackout in Ukraine is the most representative…”

Section: Introductionmentioning

confidence: 99%

A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

Cao

Wang

et al. 2020

IEEE Access

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The False data injection attack (FDIA) against the Cyber-Physical Power System (CPPS) is a kind of data integrity attack. With more and more cyber vulnerabilities detected out, different types of FDIAs are emerging as severe threats to the stable operation of CPPS gradually. In this paper, the invasion pathway of the FDIA against CPPS is explored in detail, and a novel FDIA detection model based on ensemble learning is further provided. First, a pseudo-sample database is built to assist the training and evaluation of this model, and it's more important to update the model in the future. Furthermore, the optimal feature set is extracted to characterize the behavior of the FDIA, which improves the precision of the FDIA detection model. Finally, a focal-loss-lightgbm (FLGB) ensemble classifier is constructed to detect the FDIA behavior automatically and accurately. We illustrated the performance of this model by a fusion of measurement data and power system audit logs. This model utilizes the offline training way, the conclusion shows the high precision and stability of this model, which ensures the stable operation of the smart grid and improves the FDIA resistance ability of the CPPS. INDEX TERMS CPPS, FDIA detection model, invasion pathway analysis, ensemble learning.

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Dynamic State Estimation of Generators Under Cyber Attacks

Cited by 35 publications

References 36 publications

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

Analysis of the influence of device‐to‐device communication on the access terminal of electric power data communication network

A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

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