5G-SliciNdd

This dataset repository comprises four distinct files (in arff and csv format), including the "Global" dataset, representing the combined 5G network traffic, and three individual datasets resulting from the segmentation of "Global" based on slice type (eMBB, mMTC, URLLC). These subsets offercomprehensive resources for researchers and professionals interested in network security and intrusion detection within the complex landscape of 5G networks. These datasets empower you to advance your research, develop effective intrusion detection models, and delve into the unique security challenges associated with diverse 5G network traffic profiles.This dataset is the result of an extensive research effort, primarily based on the work of Samarakoon et al., which is outlined in their reference:*Reference:<br>Samarakoon, Sehan, Yushan Siriwardhana, Pawani Porambage, Madhusanka Liyanage, Sang-Yoon Chang, Jinoh Kim, Jonghyun Kim, and Mika Ylianttila. "5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network." IEEE Dataport (December 2, 2022). doi: https://dx.doi.org/10.21227/xtep-hv36.*To produce the 5G-NIDD datasets, we embarked on a meticulous process that involved enriching the base dataset through various steps:Obtaining 5G-NIDD: We acquired the 5G-NIDD dataset, created by Samarakoon et al., which served as our foundation.Global Dataset: The "Global" dataset was created to represent the combined 5G network traffic. It includes a variety of traffic types, showcasing the diversity of the 5G environment.Slice Segmentation: The "Global" dataset was then split based on slice type, leading to the creation of three distinct subsets. These subsets are:eMBB Subset: Focusing on enhanced Mobile Broadband traffic.<br>mMTC Subset: Concentrating on massive Machine-Type Communication traffic.<br>URLLC Subset: Targeting Ultra-Reliable Low-Latency Communication traffic.This segmentation enabled us to examine and analyze intrusion detection in the context of these specific 5G traffic profiles, highlighting the unique security challenges that arise within each slice.