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

TL;DR

This paper introduces 5G-NIDD, a comprehensive, labeled dataset from a real 5G network, designed to facilitate AI/ML-based intrusion detection and improve security in next-generation wireless systems.

Contribution

It provides a new, real-world 5G network dataset for training and testing AI/ML security models, addressing the lack of such datasets in 5G security research.

Findings

ML models achieved high accuracy in intrusion detection

The dataset enables effective training of AI-based security solutions

Analysis demonstrates the dataset's utility for real-world 5G security applications

Abstract

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.