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• W4387353351 abstract "Network intrusion detection system (NIDS) is an important line of defense for network security as network attacks become more frequent. In this paper, we propose a data-driven NIDS based on feature selection and deep learning, named FS-DL. FS-DL focuses on improving data quality, using methods such as standard deviation and association rule mining to remove a large number of redundant features, reduce computational load, and improve detection accuracy. To balance detection accuracy and time cost, FS-DL uses a simple neural network structure with only three layers and minimizes the number of neurons as much as possible. Experimental results show that FS-DL only requires a small number of traffic features to obtain better detection performance. In addition, we have designed an NIDS based on FS-DL, deployed in the software-defined networking (SDN) controller for online detection of abnormal traffic." @default.
• W4387353351 created "2023-10-05" @default.
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• W4387353351 date "2023-11-01" @default.
• W4387353351 modified "2023-10-16" @default.
• W4387353351 title "A data-driven network intrusion detection system using feature selection and deep learning" @default.
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• W4387353351 doi "https://doi.org/10.1016/j.jisa.2023.103606" @default.
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