Composite and efficient DDoS attack detection framework for B5G networks

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.