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A data-driven network intrusion detection system using feature selection and deep learning
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DOI：10.1016/j.jisa.2023.103606.png)
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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.
Keyword:
Data-driven
Feature selection
Network intrusion detection
Deep learning
期刊
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3.7
论文数:
2.0K
被引数:
4.9K
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