FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

Khedr, Walid I.; Gouda, Ameer E.; Mohamed, Ehab R.

doi:10.1109/ACCESS.2023.3260256

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FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

Author(s): Walid I. Khedr ¹ , Ameer E. Gouda ¹ , Ehab R. Mohamed ¹

Publication date (Print): 2023

Journal: IEEE Access

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

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Walid I. Khedr: (View ORCID Profile)

Ameer E. Gouda: (View ORCID Profile)

Ehab R. Mohamed: (View ORCID Profile)

Journal

Title: IEEE Access

Abbreviated Title: IEEE Access

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN (Electronic): 2169-3536

Publication date Created: 2023

Publication date (Print): 2023

Volume: 11

Pages: 28934-28954

Affiliations

[1 ]Department of Information Technology, Zagazig University, Zagazig, Egypt

Article

DOI: 10.1109/ACCESS.2023.3260256

SO-VID: ed6f0165-fcc9-4ba5-9623-ddae63e9ad76

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

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Most cited references 40

Deep learning approach for Network Intrusion Detection in Software Defined Networking

Federated Learning-based Anomaly Detection for IoT Security Attacks

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

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