NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning-Reference-Cited by-同舟云学术

NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning

Published:2023 Issue: Volume:11 Page:24808-24821
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Du Jiawei¹^ORCID,Yang Kai¹^ORCID,Hu Yanjing²^ORCID,Jiang Lingjie³^ORCID

Affiliation:

1. School of Computer Science, Xijing University, Xi’an, China

2. School of Cryptographic Engineering, Engineering University of PAP, Xi’an, China

3. School of Electronic Information, Xijing University, Xi’an, China

Funder

High-level Talents Special Fund Project of Xijing University in 2022, “Research on Industrial Internet of Things Intrusion Detection Technology Based on Deep Learning”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10064274.pdf?arnumber=10064274

Reference33 articles.

1. A Deep Learning Model for Network Intrusion Detection with Imbalanced Data

2. Anomaly-based intrusion detection system for IoT networks through deep learning model

3. An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks

4. BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

5. EEG-based emotion recognition using hybrid CNN and LSTM classification

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