Simulated IoT traffic in 5G over-the-radio network system

We simulated IoT traffic, including attack traffic, to develop a security mechanism using machine learning and deep learning techniques. The simulation was conducted with a real 5G core and user equipment (UE) simulator provided by Amarisoft, with connections established over the radio.The goal of this dataset is to enhance the existing IoT datasets for 5G and to align with the expectations set by the 3GPP standards for 5G massive Machine Type Communications (mMTC) networks. We generated the dataset at least twice, with slight variations between each generated dataset. One set is intended for training, and the other for testing.This approach to data generation addresses the common issue encountered when splitting a single dataset into training and testing sets, where differences between the two are often minimal or nonexistent. The dataset is designed to train a model on raw network packets for real-time detection.