An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset-Reference-Cited by-同舟云学术

An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset

Published:2020-08 Issue: Volume:177 Page:107315
ISSN:1389-1286
Container-title:Computer Networks
language:en
Short-container-title:Computer Networks

Author:

Zhang Hongpo,Huang Lulu,Wu Chase Q.,Li Zhanbo

Funder

Xinjiang Production and Construction Corps

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference50 articles.

1. Cisco, Cisco visual networking index: global mobile data traffic forecast update, 2017–2022, 2019, (https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html). Online; accessed 18 February 2019.

2. Machine learning and deep learning methods for intrusion detection systems: a survey;Liu;Appl. Sci.,2019

3. W. Haider, J. Hu, J. Slay, B.P. Turnbull, Y. Xie, Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling, J. Netw. Comput. Appl. 87(2017) 185–192.

4. An approach for host-based intrusion detection system design using convolutional neural network;Tran,2018

5. Comparative analysis of ML classifiers for network intrusion detection;Mahfouz,2020

Cited by 208 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lightweight intrusion detection model based on CNN and knowledge distillation;Applied Soft Computing;2024-11

2. Building an intrusion detection system on UNSW‐NB15: Reducing the margin of error to deal with data overlap and imbalance;Concurrency and Computation: Practice and Experience;2024-08-22

3. A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network;Future Internet;2024-07-25

4. A Comprehensive Analysis of Explainable AI for Malware Hunting;ACM Computing Surveys;2024-07-11

5. Two-phase cost-sensitive-learning-based framework on customer-side quality inspection for TFT-LCD industry;Journal of Intelligent Manufacturing;2024-07-07