A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT

Aboelwafa, Mariam M. N.; Seddik, Karim G.; Eldefrawy, Mohamed Hamdy; Gadallah, Yasser; Gidlund, Mikael

doi:10.1109/jiot.2020.2991693

Cited by 112 publications

(67 citation statements)

References 22 publications

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“…The FDI attack can result in various outcomes relying on the intention of the intruder, which comprises error in the locational marginal prices (LMP) for illegal market profits, energy theft, along with physical destruction through the network. FDI attacks can affect the LMP by confusing the state estimation, which then unsympathetically involves the contingency analysis processes [45]. • Insertion of worms or malware can range from malicious software that operates in backgrounds to decelerate the smart grid computers' operations via employing Trojan software for stealing the certificates of practical security [46].…”

Section: A Causes Of Cyber-attacksmentioning

confidence: 99%

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CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

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Section: A Causes Of Cyber-attacksmentioning

confidence: 99%

“…Exploiting the wireless capabilities to control the industrial control system application remotely [5], [157], [165], [212] Disturbing the industrial control system operation Physical threats Spoofing a temperature sensor in a specific environment [53], [39], [45], [63], [164] Sending deceptive, false measurements to the control center…”

Section: Criminal Threatsmentioning

confidence: 99%

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

et al. 2021

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“…In SVM, we sketch data items by the point in an ndimensional area where n represents the considered features [25]. It creates a hyper plane and separates the data into classes [26].…”

Section: ) Support Vector Machinementioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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“…DBNs are compositions of simple, unsupervised networks such as restricted Boltzmann machines or autoencoders [127]. The authors in [128] utilised autoencoder networks for the detection of FDIA leveraging temporal and spatial sensor data correlations. He et al [108] proposed a DBN and state vector estimator (SVE) for real‐time detection of FDIAs.…”

Section: Machine Learning For Fdias Detectionmentioning

confidence: 99%

Survey of machine learning methods for detecting false data injection attacks in power systems

Sayghe

Hu²,

Zografopoulos

et al. 2020

IET Smart Grid

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A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT

Cited by 112 publications

References 22 publications

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

CPS Attacks Mitigation Approaches on Power Electronic Systems With Security Challenges for Smart Grid Applications: A Review

Attack Detection in IoT using Machine Learning

Survey of machine learning methods for detecting false data injection attacks in power systems

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