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- W2892847744 abstract "In this paper an intrusion detection model is constructed based on the long short-term neural networks. Training of the model and tests on its performance are done with KDD 99 and UNSW-NB15 data-sets by choosing the optimum parameters via experiments. What's more, a comparison is done with the traditional machine learning model. The results show that this intrusion detection model has a higher detection accuracy than the traditional machine learning models. The detection accuracy with respect to the two data-sets is 98.99% and 99.41% respectively." @default.
- W2892847744 created "2018-10-05" @default.
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- W2892847744 date "2018-06-01" @default.
- W2892847744 modified "2023-10-16" @default.
- W2892847744 title "Constructing an Intrusion Detection Model based on Long Short-term Neural Networks" @default.
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- W2892847744 doi "https://doi.org/10.1109/icis.2018.8466445" @default.
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