Feature Selection and Model Evaluation for Threat Detection in Smart Grids
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References listed on IDEAS
- Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.
- Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
- R Vinayakumar & K.P. Soman & Prabaharan Poornachandran, 2017. "Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(3), pages 43-63, July.
- Shahid Tufail & Imtiaz Parvez & Shanzeh Batool & Arif Sarwat, 2021. "A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid," Energies, MDPI, vol. 14(18), pages 1-22, September.
- Brandon Butcher & Brian J. Smith, 2020. "Feature Engineering and Selection: A Practical Approach for Predictive Models," The American Statistician, Taylor & Francis Journals, vol. 74(3), pages 308-309, July.
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- Marek Natkaniec & Jakub Dyrcz, 2024. "StegoDCF: A New Covert Channel for Smart Grids Utilizing the Channel Access Procedure in Wi-Fi Networks," Energies, MDPI, vol. 17(9), pages 1-26, April.
- Geovani Teca & Marek Natkaniec, 2024. "StegoBackoff: Creating a Covert Channel in Smart Grids Using the Backoff Procedure of IEEE 802.11 Networks," Energies, MDPI, vol. 17(3), pages 1-26, February.
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Keywords
smart grids; network anomalies; threat detection; feature selection; machine learning; performance metrics;All these keywords.
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