Detection of DoS and DDoS attacks on 5G network slices using deep learning approach
A new degree of connectedness and interaction has been introduced by the development of 5G networks. By dividing a physical network into several logical networks, 5G network slicing is a special feature that gives network operators the ability to allocate specific resources and services to various applications and customers. However, 5G network slicing is susceptible to cyberattacks, particularly Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attacks, just like any other network. Such attacks can have a significant negative effect on network performance, degrading services and reducing the availability of slices. The primary objective of this thesis is to examine the impact of DoS/DDoS attacks on 5G network slicing and their potential to disrupt the performance of legitimate users and slice availability. Additionally, a novel dataset specifically tailored to DoS/DDoS attacks in 5G network slicing is generated, as there is no available dataset based on a 5G network slice. Through extensive research, key features relevant to DoS/DDoS attacks are identified and prioritized. To categorize and detect different types of DoS/DDoS attacks, two deep learning techniques, namely the convolutional neural network (CNN) and the Bidirectional Long Short-Term Memory (BLSTM) models, are employed. These models not only utilize the newly created dataset but also enable comparison with existing datasets to assess their effectiveness. This thesis emphasizes how crucial it is to create strong security measures to guard against DoS/DDoS attacks on 5G network slicing. A step in the right direction toward reaching this goal is the construction of a deep learning model for the classification, detection, and production of a new dataset specifically for 5G network slicing. To keep enhancing the security and stability of 5G network slicing, more study in this area will be required. The results indicate that the proposed models have a high accuracy rate of 99.96% in distinguishing different types of DoS/DDoS attacks within the networking slice environment. This achievement is noteworthy as it pertains to a novel context. Additionally, the newly developed models exhibit comparable performance in terms of other confusion metrics. To verify the research outcome, some well-known data sets are used to show the results.
Networked Digital Library of Theses and Dissertations
Faculty of Graduate Studies and Research, University of Regina