DoS/DDoS Attack Dataset of 5G Network Slicing

Citation Author(s):
Md Sajid
Khan
University of Regina, Canada
Behnam
Farzaneh
University of Regina, Canada
Nashid
Shahriar
University of Regina, Canada
Md Mahibul
Hasan
University of Regina, Canada
Submitted by:
MD SAJID KHAN
Last updated:
Sat, 02/03/2024 - 08:48
DOI:
10.21227/32k1-dr12
Data Format:
*.csv; *.pcap; *.txt; *.zip
Research Article Link:
SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices
Links:
SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices
Detection of DoS and DDoS attacks on 5G network slices using deep learning approach
DTL-IDS: Deep Transfer Learning-Based Intrusion Detection System in 5G Networks
License:
Creative Commons Attribution
3801 Views
Categories:
Wireless Networking
Security
Keywords:
Security, 5G, DoS, DDoS, network, network security,  wireless, Cybersecurity, 5G dataset, network slicing, 5g network slicingattack, Machine Learning, Deep Learning
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Abstract 

5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices. In this paper, we first show how DoS/DDoS attacks on network slices can impact slice users' performance metrics such as bandwidth and latency. Then, we present a novel DoS/DDoS attack dataset collected from a simulated 5G network slicing test bed. Finally, we showed a deep-learning-based bidirectional LSTM (Long Short Term Memory) model, namely, SliceSecure can detect DoS/DDoS attacks with an accuracy of 99.99% on the newly created data sets for 5G network slices.

Instructions: 

This dataset is an asset for researchers and professionals focusing on the security of 5G networks, specifically in the context of network slicing. It offers a comprehensive range of data, including different types of benign and attack traffic, important for the development and evaluation of intrusion detection approaches. The dataset is particularly beneficial for studies in the following areas:

5G Network Slicing: It provides real-world scenarios of network traffic in 5G network slicing, making it suitable for research in this emerging field.

Machine Learning and Deep Learning: The dataset is ideal for developing and testing machine learning and deep learning models, particularly in the context of network security.

Transfer Learning: As demonstrated in the associated research, the dataset is well-suited for experiments in transfer learning, especially in adapting models trained on different types of network environments to the specific challenges of 5G network slicing.

Intrusion Detection System: The detailed and varied nature of the dataset makes it a main resource for designing and testing intrusion detection systems suitable for 5G networks.

Funding Agency: 
Innovation for Defence Excellence and Security (IDEaS) program from the Department of National Defence (DND)

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good

Submitted by Pritam Vediya on Sat, 12/07/2024 - 06:58

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