Why Dealing with Electrical Faults for Smart Microgrid is not Enough?

Gupta, Pragya Kirti; Narayanan Babu, Sai Shibu; Mohandas Sheeladevi, Anjana; Pampana, Venkatesh

doi:10.1007/978-3-031-06371-8_5

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…Now, the objective function of NCA is maximized using LOO classification with stochastic neighbor selection rule, which can be expressed in terms of the cost function f (A) in (5). This cost function f (A) pulls points from the same class closer together.…”

Section: Neighborhood Components Analysis In the Context Of Power Domainmentioning

confidence: 99%

“…The mission-critical infrastructure of Cyber-Physical Power Systems [1] (CPPS), such as a smart grid, has been targeted for cyberwarfare to cause physical sabotage, large-scale load loss, blackouts, and cascading failures [2][3][4]. In most cases of power grid disturbances, fault analysis and diagnosis [5] are conducted using state estimation methods [6] and time-series analysis [7]. However, the continuously increasing rate of network traffic makes it difficult for cyber analysts to spot new patterns of behavior in the network.…”

Section: Introductionmentioning

confidence: 99%

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Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Murugesan,

Velu,

Palaniappan

et al. 2024

Energies

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In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

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Section: Neighborhood Components Analysis In the Context Of Power Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Murugesan,

Velu,

Palaniappan

et al. 2024

Energies

View full text Add to dashboard Cite

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Towards Early Cyberattack Discrimination in the Smart Grid Using Statistical Approach with Machine Learning Techniques

et al. 2023

Preprint

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Smart Grid has been exposed to cyberattacks that penetrate the Supervisory Control and Data Acquisition systems, causing privacy and access control violations on security infrastructures. It is critical to address this issue with an early attack detection mechanism with a reduced missing rate and less response time that discriminates cyberattacks against natural and man-made events. Developing a robust intrusion detection system is still a challenging research problem to prevent the smart grid system from sabotage and processing downtime. This paper aims to develop a robust Anomaly-Based Intrusion Detection System using the statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of Statistical Approach with a Machine Learning classifier (SAML) based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting respectively is proposed, to optimally tune the hyperparameters for early discrimination of Triple class events. The proposed model is tested on the publicly available Industrial Control Systems Cyber Attack Power System Triple Class Dataset. The test results provide a higher detection accuracy with a low missing rate, false alarm rate, and less response time than the existing approaches.

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Why Dealing with Electrical Faults for Smart Microgrid is not Enough?

Cited by 2 publications

References 39 publications

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Towards Early Cyberattack Discrimination in the Smart Grid Using Statistical Approach with Machine Learning Techniques

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