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Reinforcement learning-based slice isolation against ddos attacks in beyond 5g networks

Javadpour, A ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/TNSM.2023.3254581
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. Network slicing in 5G networks can be modeled as a Virtual Network Embedding (VNE) problem, wherein the slice requests must be efficiently mapped on the core network. This process faces two major challenges: covering the maximum number of requests and providing slice isolation. Slice isolation is a mechanism for protecting the slices against Distributed Denial of Service (DDoS) attacks. To overcome these two challenges, we have proposed a novel actor-critic Reinforcement Learning (RL) model, called Slice Isolation-based Reinforcement Learning (SIRL), using five optimal graph features to create the problem environment, the form of which is changed based on a ranking scheme. The ranking procedure reduces the dimension of the features and improves learning performance. We evaluated SIRL by comparing it against four non-RL and nine state-of-the-art RL models. The average results show that the ratio of the covered requests and the damage caused by a DDoS attack of SIRL is 54% higher and 23% lower than that of the other models, respectively. It also has an acceptable learning performance and generality, regarding the reported results that show SIRL agents trained and tested with different networks outperform the other agents by 97%. © 2004-2012 IEEE
  6. Keywords:
  7. 5G networks ; Distributed denial of service (DDoS) ; Reinforcement learning (RL) ; Slice isolation
  8. Source: IEEE Transactions on Network and Service Management ; Volume 20, Issue 3 , 2023 , Pages 3930-3946 ; 19324537 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/10064361