Detection of False Data Injection Attacks in Smart Grid Utilizing ELM-Based OCON Framework

Xue, Dongbo; Jing, Xiaorong; Liu, Hongqing

doi:10.1109/access.2019.2902910

Cited by 89 publications

(43 citation statements)

References 41 publications

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“…This kind of method doesn't need to solve complex time-domain equations of the power system, and clear performance indicators can be used to evaluate the performance, it is one of the current main FDIA detection methods. This type of method mainly includes support vector machine (SVM) [18], extreme learning machine [19], fuzzy C-means clustering [20], deep learning [21], ensemble learning, etc., [22]. The detection performance of traditional supervised learning algorithms such as SVM depends on the quality of the data heavily, such as the poor characterization ability of the feature set will cause a low detection rate.…”

Section: The Detection Methods Based On Machine Learningmentioning

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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Section: The Detection Methods Based On Machine Learningmentioning

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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“…This can be done by using the kernel-independent component analysis to map the restricted data into a new Jacobian matrix, through which the undetectable attack is modeled [139]. [140] proposed an extreme learning machine (ELM) technique based on one-class-one-network (OCON) framework to detect any cyber threat on the AC state estimation. FDIA attacks are detected using Kullback-Leibler Distance in [141], where the accuracy of the detection mechanism is influenced by the predefined thresholds.…”

Section: A Fdia Detectionmentioning

confidence: 99%

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

et al. 2020

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The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare. INDEX TERMS Cyber physical systems (CPS), cyber attack, intrusion detection system (IDS), false data injection attack (FDIA), energy management system (EMS), power grid resilience.

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“…In recent years, cyber attacks have become a hot topic in power system studies. In [21], a detection model based on extreme learning machine was proposed to test and identify FDI attacks. A DSEbased risk mitigation strategy was presented for eliminating threat levels from cyber attacks in [22].…”

Section: Introductionmentioning

confidence: 99%

Dynamic State Estimation of Generators Under Cyber Attacks

Yang

Liang

2019

IEEE Access

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Accurate and reliable estimation of generator's dynamic state vectors in real time are critical to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) based approach is proposed for dynamic state estimation (DSE) of generators under cyber attacks in this paper. First, two types of cyber attacks, namely false data injection and denial of service attacks, are modelled and thereby introduced into DSE of a generator by mixing the attack vectors with the measurement data; Second, under cyber attacks with different degrees of sophistication, the RCKF algorithm and the Cubature Kalman Filter (CKF) algorithm are adopted to the DSE, and then the two algorithms are compared and discussed. The novelty of this study lies primarily in our attempt to introduce cyber attacks into DSE of generators. The simulation results on the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness and superiority of the RCKF. INDEX TERMS Dynamic state estimation, cyber attacks, false data injection, denial of service, generator, robust cubature Kalman filter, PMU data

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Detection of False Data Injection Attacks in Smart Grid Utilizing ELM-Based OCON Framework

Cited by 89 publications

References 41 publications

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

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

Electric Power Grid Resilience to Cyber Adversaries: State of the Art

Dynamic State Estimation of Generators Under Cyber Attacks

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