Enhancing Security in 5G Networks: A Hybrid Machine Learning Approach for Attack Classification
Author:
Affiliation:
1. Polytechnic Institute Utica,Networks Computer Security,NY,USA
2. University of Paris VIII,Paragraphe Research Lab,Paris,France
3. Staffordshire University,Digital Technologies and Art,Staffordshire,UK
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10479189/10479228/10479294.pdf?arnumber=10479294
Reference43 articles.
1. Network Anomaly Detection in 5G Networks
2. A triangular fuzzy based multicriteria decision making approach for assessing security risks in 5g networks;Kholidy,2021
3. A wireless intrusion detection for the next generation (5g) networks;Ferrucci,2020
4. Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach
5. Autonomous mitigation of cyber risks in the Cyber–Physical Systems
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