Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest

Ahmed, Saeed; Lee, Young-Doo; Hyun, Seung-Ho; Koo, Insoo

doi:10.1109/tifs.2019.2902822

Cited by 198 publications

(92 citation statements)

References 59 publications

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“…As mentioned above, the CCDA can be modeled to deceive the BDD with the ultimate objective being to falsify single or multiple system states. We assumed that the attacker has complete knowledge of the topology of the power network, and the attack is initiated following the model described in [6]. The attack formulation is explained in Section 2.…”

Section: Power System Data and Attack Data Generationmentioning

confidence: 99%

“…Thus, the time consumed in detection can be saved; however, all measurement samples are reconstructed in this scheme, whether they were attacked or not. To extend our previous work from detection [6,25,26] to mitigation, in this paper, we propose a deep neural network (DNN)-based data reconstruction scheme (Scheme-III) to mitigate the impacts of a CCDA on the SG's measurement dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.Energies 2019, 12, 3091 2 of 24 disruptions [4,5]. Owing to the harmful impacts of such attacks on the reliable and secure operations of SGs, there is a need to investigate counter attack measures.Generally, the defense measures reported in the literature can be organized into three layers: protection, detection, and mitigation [6].Extensive investigations have been reported in the literature on the detection layer of the defense mechanism. Table 1 shows a summary of the research works carried out on the detection and mitigation tiers.…”

mentioning

confidence: 99%

“…Generally, the defense measures reported in the literature can be organized into three layers: protection, detection, and mitigation [6].…”

mentioning

confidence: 99%

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Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

et al. 2019

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As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

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Section: Power System Data and Attack Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…Generally, the defense measures reported in the literature can be organized into three layers: protection, detection, and mitigation [6].…”

mentioning

confidence: 99%

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Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

et al. 2019

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“…In addition, data-driven approaches have recently been applied to FDI attacks, although mostly from the defenders perspective [10] [11]. In [12], singular value decomposition is used to construct attack vectors without knowing the underlying system measurement matrix.…”

Section: Introductionmentioning

confidence: 99%

Stealthy MTD Against Unsupervised Learning-Based Blind FDI Attacks in Power Systems

Higgins

Teng

Parisini

2021

IEEE Trans.Inform.Forensic Secur.

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This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability.

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False Data Injection Attacks in Power Systems

Ameli¹,

Hamed²

2023

Wiley Encyclopedia of Electrical and Electronics Engineering

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Power networks are among the most critical infrastructures of any society, since other infrastructures are dependent on power and energy systems. Recently, there has been a trend toward harnessing Information Technology (IT) in power networks to enhance their performance and improve their consumer centricity. This movement, however, has exposed power systems to cyberattacks. Among various types of cyber intrusions, False Data Injection Attack (FDIA) is the most important one, since it stealthily intercepts the legitimate traffic and/or injects malicious data into the system, thus resulting in severe physical and economic damages. There has been a substantial increase in the number of reported FDIAs in recent years, which indicates the necessity of extensive research on vulnerabilities of power systems to FDIAs, probable attack models, impacts of such intrusions, and the possible preventive and detective measures. On this basis, this article presents a comprehensive review of FDIAs in power networks, with a particular focus on attack targets and adversarial methods. Additionally, it elaborates on various types of FDIAs, their implementation steps and strategies, and the vulnerabilities of various components and schemes to this type of attack. Finally, the it briefly discusses FDIA detection techniques in power grids.

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Unsupervised Machine Learning-Based Detection of Covert Data Integrity Assault in Smart Grid Networks Utilizing Isolation Forest

Cited by 198 publications

References 59 publications

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

Stealthy MTD Against Unsupervised Learning-Based Blind FDI Attacks in Power Systems

False Data Injection Attacks in Power Systems

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