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- W4361761223 abstract "The widespread use of smart devices (smartphones, smart locks, etc.), and the rush by companies to digitize their resources, as well as the lack of awareness among users about the dangers around us while surfing the web, have left the world full of vulnerabilities that have created a fertile ground for hackers to try all kinds of hard-to-detect attack techniques. On the other side, cybersecurity researchers have redoubled their efforts to develop effective intrusion detection systems, capable of detecting not only well-known attacks but also ones that have never been seen before. This paper will highlight the different stages of designing Network Intrusion Detection Systems (NIDS) using Machine Learning (ML) techniques, including, benchmark datasets, feature reduction, hyperparameter optimization, detection methods, and evaluation metrics. In addition, we will conduct a detailed study of recent articles (2018–2022) in which we will discuss each work’s strengths and shortcomings. Finally, we will use the drawbacks of proposed approaches to list the difficulties that researchers can face while developing ML-based NIDS." @default.
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- W4361761223 date "2023-01-01" @default.
- W4361761223 modified "2023-09-30" @default.
- W4361761223 title "Machine Learning-Based Intrusion Detection System: Review and Taxonomy" @default.
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- W4361761223 doi "https://doi.org/10.1007/978-3-031-28387-1_2" @default.
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