A homogeneous ensemble based dynamic artificial neural network for solving the intrusion detection problem
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DOI: 10.1016/j.ijcip.2021.100449
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References listed on IDEAS
- Genge, Béla & Haller, Piroska & Kiss, István, 2016. "A framework for designing resilient distributed intrusion detection systems for critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 15(C), pages 3-11.
- Choubineh, Abouzar & Wood, David A. & Choubineh, Zahak, 2020. "Applying separately cost-sensitive learning and Fisher's discriminant analysis to address the class imbalance problem: A case study involving a virtual gas pipeline SCADA system," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
- Morris, Thomas & Srivastava, Anurag & Reaves, Bradley & Gao, Wei & Pavurapu, Kalyan & Reddi, Ram, 2011. "A control system testbed to validate critical infrastructure protection concepts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 4(2), pages 88-103.
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- Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz & Suvendi Rimer & Kuburat Oyeranti Adefemi Alimi, 2021. "A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
- Shuai Liu & Hui Qin & Guanjun Liu & Yang Xu & Xin Zhu & Xinliang Qi, 2023. "Runoff Forecasting of Machine Learning Model Based on Selective Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4459-4473, September.
- Mohammed Abdul Majeed & Rossilawati Sulaiman & Zarina Shukur & Mohammad Kamrul Hasan, 2021. "A Review on Text Steganography Techniques," Mathematics, MDPI, vol. 9(21), pages 1-28, November.
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Keywords
Classification; Optimization; Filter-wrapper feature selection; Dynamic artificial neural network; Intrusion detection system; Critical infrastructure;All these keywords.
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