Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach

Author:

Kabir Mohammad Humayun1,Rajib Md Shahriar1,Rahman Abu Saleh Md Towfiqur1,Rahman Md. Mahbubur1,Dey Samrat Kumar2

Affiliation:

1. Military Institute of Science and Technology,Department of CSE,Dhaka,Bangladesh

2. School of Science and Technology, Bangladesh Open University,Gazipur,Bangladesh

Publisher

IEEE

Cited by 7 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. Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis;Algorithms;2024-02-01

2. Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things;Sensors;2023-12-26

3. An Efficient Hierarchical LSTM-based Framework for Intrusion Detection in Internet of Things (IoT) Systems;2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM);2023-09-21

4. Comparative Analysis of Feed-Forward and RNN Models for Intrusion Detection in Data Network Security with UNSW-NB15 Dataset;2023 33rd International Conference Radioelektronika (RADIOELEKTRONIKA);2023-04-19

5. Network Intrusion Detection: A Study on Various Learning Approaches;2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE);2023-03-09

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