Threat Intelligence with Non-IID Data in Federated Learning enabled Intrusion Detection for SDN: An Experimental Study-Reference-Cited by-同舟云学术

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Threat Intelligence with Non-IID Data in Federated Learning enabled Intrusion Detection for SDN: An Experimental Study

Published:2023-12-06 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 24th International Arab Conference on Information Technology (ACIT)
language:
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Author:

Kazmi Syed Hussain Ali¹,Qamar Faizan¹,Hassan Rosilah¹,Nisar Kashif²,Dahnil Dahlila Putri Binti³,Al-Betar Mohammed Azmi⁴

Affiliation:

1. Universiti Kebangsaan Malaysia,Center for Cyber Security,Selangor,Malaysia

2. Swinburne University of Technology,Sydney,New South Wales,Australia,2150

3. Universiti Kebangsaan Malaysia,Software Technology & Management Research Center (Softam),Selangor,Malaysia

4. College of Engineering and Information Technology, Ajman University,Artificial Intelligence Research Center (AIRC),Ajman,UAE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10453649/10453660/10453867.pdf?arnumber=10453867

Reference27 articles.

1. Survey on Joint Paradigm of 5G and SDN Emerging Mobile Technologies: Architecture, Security, Challenges and Research Directions

2. A Study on Multi-Antenna and Pertinent Technologies with AI/ML Approaches for B5G/6G Networks

3. Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions

4. Security in Federated Learning Enabled 6g Era: A Review on Conceptual Techniques and Software Platforms Used for Research and Analysis;Kazmi;Available at SSRN 4587651

5. A survey of network-based intrusion detection data sets

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