2023
DOI: 10.1109/access.2023.3254915
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NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning

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Cited by 45 publications
(14 citation statements)
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References 30 publications
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“…J. Du, K. Yang, Y. Hu, and L. Jiang, [14] present NIDS-CNNLSTM, a computationally demanding intrusion detection and classification system for IIoT wireless sensing. These studies demonstrate the variety of methodologies employed for IDS creation, each with advantages and disadvantages in dealing with the always changing cyber threat environment.…”
Section: A Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…J. Du, K. Yang, Y. Hu, and L. Jiang, [14] present NIDS-CNNLSTM, a computationally demanding intrusion detection and classification system for IIoT wireless sensing. These studies demonstrate the variety of methodologies employed for IDS creation, each with advantages and disadvantages in dealing with the always changing cyber threat environment.…”
Section: A Discussionmentioning
confidence: 99%
“…For the IIoT wireless sensing scenario, a DL-based network intrusion identification and categorization model (NIDS-CNNLSTM) is created in the J. Du, K. Yang, Y. Hu, and L. Jiang, [14] goal is to effectively distinguish and recognize network traffic while ensuring the equipment and operation of the IIoT are secure. Using LSTM in data from time series together with the powerful capacity for learning of neural networks, NIDS-CNNLSTM trains and classifies the features selected by the CNN and verifies its application through binary categorisation and multi-classification scenarios.…”
Section: Related Workmentioning
confidence: 99%
“…Besides, equilibrium optimization with hybrid CNN-LSTM (HCNN-LSTM) assisted classification method could be obtained to recognize the intrusions. In [14], a network intrusion detection classification method (NIDS-CNNLSTM) depends on DL has been implemented. This approach integrates the robust learning capability of LSTM-NN in time series data, categorizes and learns the chosen features via the CNN, and confirms the applicability dependent upon multi-classification and binary classification conditions.…”
Section: ░ 2 Related Workmentioning
confidence: 99%
“…In order to further verify the validity and feasibility of the model, a comparative experiment was set up under the same dataset and experimental environment, compared with CNN-LSTM [30], GDP-FL [10], LATENT [11], and PLU-FedOA [13] for comparison. To prove that the model performance of FLM-ICR is superior to other methods and has better model performance while protecting data privacy.…”
Section: Comparative Experimentsmentioning
confidence: 99%