Network Intrusion Detection Methods Based on Deep Learning

Author(s): Xiangwen Li, Shuang Zhang*

Journal Name: Recent Patents on Engineering

Volume 15 , Issue 4 , 2021


Article ID: e210421180688
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Abstract:

To detect network attacks more effectively, this study uses Honeypot techniques to collect the latest network attack data and proposes network intrusion detection classification models, based on deep learning, combined with DNN and LSTM models. Experiments showed that the data set training models gave better results than the KDD CUP 99 training model’s detection rate and false positive rate. The DNN-LSTM intrusion detection algorithm, proposed in this study, gives better results than KDD CUP 99 training model. Compared to other algorithms, such as LeNet, DNNLSTM intrusion detection algorithm exhibits shorter classification test time along with better accuracy and recall rate of intrusion detection.

Keywords: Honeypot, intrusion protection, intrusion detection, deep learning, network security, DNN-LSTM.

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Article Details

VOLUME: 15
ISSUE: 4
Year: 2021
Published on: 04 May, 2021
Article ID: e210421180688
Pages: 9
DOI: 10.2174/1872212114999200403092708
Price: $25

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