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Digital Twin-Enabled Intelligent DDoS Detection Mechanism For Autonomous Core Networks

Yigit, Yagmur; Bal, Bahadir; Karameseoglu, Aytac; Duong, Trung Q.; Canberk, Berk


Yagmur Yigit

Bahadir Bal

Aytac Karameseoglu

Trung Q. Duong


Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are threefold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently , improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts.

Journal Article Type Article
Online Publication Date Sep 26, 2022
Publication Date 2022-09
Deposit Date Nov 1, 2022
Journal IEEE Communications Standards Magazine
Print ISSN 2471-2825
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 6
Issue 3
Pages 38-44
Public URL