A Comparative Study of Deep Learning Techniques for Network Intrusion Detection
Author:
Affiliation:
1. KIIT University,School of Computer Engineering,Bhubaneswar,INDIA
2. Oil and Natural Gas Coorporation Ltd,Geodata Processing and Interpretation Center,Dehradun,INDIA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10481518/10481530/10481540.pdf?arnumber=10481540
Reference10 articles.
1. Critical Analysis of 5G Networks’ Traffic Intrusion Using PCA, t-SNE, and UMAP Visualization and Classifying Attacks
2. Deep-intrusion detection system with enhanced UNSW-NB15 dataset based on deep learning techniques;Aleesa;Journal of Engineering Science and Technology,2021
3. 5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network;Samarakoon;arXiv preprint arXiv:2212.01298,2022
4. Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset
5. Automatic network intrusion detection: Current techniques and open issues
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