Accurate Automata-Based Detection of Cyber Threats in Smart Grid Communication

Havlena, Vojtěch; Matoušek, Petr; Ryšavý, Ondřej; Holík, Lukáš

doi:10.1109/tsg.2022.3216726

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…In 2021, Siniosoglou et al [6] have presented the IDS technique for effective anomaly detection in smart grids with transmission control protocols. Here, the IDS techniques were widely employed to perform effective anomaly identification and categorizations using an adversarial network structure in identifying the cyber attacks.…”

Section: A Related Workmentioning

confidence: 99%

“…Due to the non-optimum value, the system cannot reach the target detection value. DPAs [6] enhance the true rate and deduce the false rate. Still, it contains inadequate feature information, affecting the system's efficacy.…”

Section: B Problem Statementmentioning

confidence: 99%

“…• Due to the non-optimum value, the system cannot reach the target detection value. Havlena et al [6] DPAs • It enhances the true rate and deduces the false rate.…”

mentioning

confidence: 99%

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Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

Scholar,

(Senior)

2024

Preprint

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The integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness.

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Section: A Related Workmentioning

confidence: 99%

Section: B Problem Statementmentioning

confidence: 99%