Composite and efficient DDoS attack detection framework for B5G networks
- Authors
- Amaizu, G. C.; Nwakanma, C., I; Bhardwaj, S.; Lee, J. M.; Kim, D. S.
- Issue Date
- Apr-2021
- Publisher
- ELSEVIER
- Keywords
- Network security; 5G; DDoS; Artificial intelligence
- Citation
- COMPUTER NETWORKS, v.188
- Journal Title
- COMPUTER NETWORKS
- Volume
- 188
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21080
- DOI
- 10.1016/j.comnet.2021.107871
- ISSN
- 1389-1286
- Abstract
- Distributed denial-of-service (DDoS) remains an ever-growing problem that has affected and continues to affect a host of web applications, corporate bodies, and governments. With the advent of fifth-generation (5G) network and beyond 5G (B5G) networks, the number and frequency of occurrence of DDoS attacks are predicted to soar as time goes by, hence there is a need for a sophisticated DDoS detection framework to enable the swift transition to 5G and B5G networks without worrying about the security issues and threats. A range of schemes has been deployed to tackle this issue, but along the line, few limitations have been noticed by the research community about these schemes. Owing to these limitations/drawbacks, this paper proposes a composite and efficient DDoS attack detection framework for 5G and B5G. The proposed detection framework consists of a composite multilayer perceptron which was coupled with an efficient feature extraction algorithm and was built not just to detect a DDoS attack, but also, return the type of DDoS attack it encountered. At the end of the simulations and after testing the proposed framework with an industry-recognized dataset, results showed that the framework is capable of detecting DDoS attacks with a high accuracy score of 99.66% and a loss of 0.011. Furthermore, the results of the proposed detection framework were compared with their contemporaries.
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