# Digital Twin-Enabled Intelligent DDoS Detection Mechanism for Autonomous Core Networks

arXiv: https://arxiv.org/abs/2310.12924 · HTML: https://mlsanity.com/paper/2310.12924/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 19 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [LLM-Twin: Mini-Giant Model-driven Beyond 5G Digital Twin Networking Framework with Semantic Secure Communication and Computation](https://mlsanity.com/paper/2312.10631/) (2023)
- [Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware](https://mlsanity.com/paper/2412.14569/) (2024)

## Abstract (arXiv)

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 three-fold: 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.