# 5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network

arXiv: https://arxiv.org/abs/2212.01298 · HTML: https://mlsanity.com/paper/2212.01298/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2022.

## Built on by (most cited first, 16 of 16)

- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection](https://mlsanity.com/paper/2409.03141/) (2024)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [A Life-long Learning Intrusion Detection System for 6G-Enabled IoV](https://mlsanity.com/paper/2407.15700/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection](https://mlsanity.com/paper/2407.04009/) (2024)
- [One-Shot Collaborative Data Distillation](https://mlsanity.com/paper/2408.02266/) (2024)
- [An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning](https://mlsanity.com/paper/2410.05312/) (2024)
- [Critical Analysis of 5G Networks Traffic Intrusion using PCA, t-SNE and UMAP Visualization and Classifying Attacks](https://mlsanity.com/paper/2312.04864/) (2023)
- [Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond](https://mlsanity.com/paper/2412.03483/) (2024)
- [Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition](https://mlsanity.com/paper/2505.01783/) (2025)
- [Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparison](https://mlsanity.com/paper/2509.10206/) (2025)
- [An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection](https://mlsanity.com/paper/2510.02349/) (2025)
- [Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks](https://mlsanity.com/paper/2510.23019/) (2025)
- [GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks](https://mlsanity.com/paper/2606.05844/) (2026)
- [Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks](https://mlsanity.com/paper/2608.14826/) (2026)

## Abstract (arXiv)

With a plethora of new connections, features, and services introduced, the 5th generation (5G) wireless technology reflects the development of mobile communication networks and is here to stay for the next decade. The multitude of services and technologies that 5G incorporates have made modern communication networks very complex and sophisticated in nature. This complexity along with the incorporation of Machine Learning (ML) and Artificial Intelligence (AI) provides the opportunity for the attackers to launch intelligent attacks against the network and network devices. These attacks often traverse undetected due to the lack of intelligent security mechanisms to counter these threats. Therefore, the implementation of real-time, proactive, and self-adaptive security mechanisms throughout the network would be an integral part of 5G as well as future communication systems. Therefore, large amounts of data collected from real networks will play an important role in the training of AI/ML models to identify and detect malicious content in network traffic. This work presents 5G-NIDD, a fully labeled dataset built on a functional 5G test network that can be used by those who develop and test AI/ML solutions. The work further analyses the collected data using common ML models and shows the achieved accuracy levels.