DeepFedKDAD: Deep Federated Knowledge Distillation Based Anomaly Detection in B5G Network Slicing-Reference-Cited by-同舟云学术

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DeepFedKDAD: Deep Federated Knowledge Distillation Based Anomaly Detection in B5G Network Slicing

Published:2023-11-17 Issue: Volume: Page:146-151
ISSN:
Container-title:2023 International Conference on Networks, Communications and Intelligent Computing (NCIC)
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Author:

Elorm Kuadey Noble Arden¹,Maale Gerald Tietaa¹,Sun Guolin¹,Liu Guisong²

Affiliation:

1. Sch. of Comp. Sci and Eng., UESTC, Chengdu, China Nat. Key Lab. for Intel. Collab. Comp., Chengdu, China Intel. Terminal Key Lab. of Sichuan Prov., Yibin, China,Chengdu,China

2. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics,Chengdu,China

Funder

Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Yibin Science and Technology Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10529626/10529627/10529689.pdf?arnumber=10529689

Reference15 articles.

1. Distributed Online Anomaly Detection for Virtualized Network Slicing Environment

2. Cooperative Anomaly Detection With Transfer Learning-Based Hidden Markov Model in Virtualized Network Slicing

3. A Many-Objective Optimization Based Intelligent Intrusion Detection Algorithm for Enhancing Security of Vehicular Networks in 6G

4. A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

5. MLTs-ADCNs: Machine Learning Techniques for Anomaly Detection in Communication Networks

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