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- W4384434070 abstract "The SCADA systems in the Smart Grid Network (SGN) are increasingly facing cyber threats and divers attacks due to their known proprietary vulnerabilities, most often leading to power instability and cascading failures in the Grid. This paper associates the SGN’s instability with cyberattacks and models intrusions into SCADA systems using the Decentral Smart Grid Control (DSGC) model on the Stability dataset. The Time-Synchronization Attack was modelled with its effects on Transmission Line Fault Detection, Voltage Stability Monitoring and Event Locating. Classification and prediction were done on WEKA using a number of Machine Learning Algorithms and performances evaluated. Random Forest (RF) classifier was found to be more promising in all the benefit metrics yielding 92.0%, 91.9%, 92.0%, and 91.9% for Accuracy, Precision, Recall and F-measure, respectively. An ensemble classifier – Bagging, performed significantly well after the RF with 90.0%, 89.9%, 90.0% and 89.9%. The ROC Area under Curve also showed RF having 97% and Bagging 96.7%. 100 trees were constructed for the RF while considering 4 random features, and the out-of-bag error was 0.0084. RF had a TPR of 92.0%, which is the highest and an FPR of 0.11. These results provide insights into identifying appropriate ML techniques and models for the Grid’s instability problem vis-à-vis modelling attacks. This is expected to help in effective security planning, threats detection, attack mitigation and risk management." @default.
- W4384434070 created "2023-07-16" @default.
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- W4384434070 date "2023-01-01" @default.
- W4384434070 modified "2023-09-26" @default.
- W4384434070 title "Modelling Smart Grid Instability Against Cyber Attacks in SCADA System Networks" @default.
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- W4384434070 doi "https://doi.org/10.1007/978-3-031-36115-9_23" @default.
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