Distributed Denial-Of-Service (DDoS) in Software-Defined Network Based on Artificial Neural Network and Binary Multi-Neighborhood Artificial Bee Colony (BMNABC) Algorithm
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, an intelligent intrusion detection system in a software-based network with metaheuristic algorithm is presented. In the proposed system, the controllers use the binary multi-neighborhood artificial bee colony Algorithm, to select the features. In the proposed method, the artificial neural network is used to classify the attach and non-attach data. For comparison of the results the machine learning algorithm such as support vector machine, decision tree, Ensemble, Navie Base, and K-NN methods has been used and evaluated. the best results have been obtained for artificial neural network and it was 98.99%, 98.97%, 98.94%, 98.95%, and 97.95% for accuracy of train, accuracy of test, precision test, and recall test, and F1 test, respectively. Also, the worst results have been obtained for the DT method and it was 97.54%, 97.56%, 97.42%, 97.76%, and 97.09% for accuracy of train, accuracy of test, precision test, and recall test, and F1 test respectively. © 2023 IEEE.
Açıklama
3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023 -- 1 December 2023 through 2 December 2023 -- Hassan -- 196843
Anahtar Kelimeler
Artificial Neural Network, Binary multi-neighborhood artificial bee colony Algorithm, Distributed denial-of-service, Software-Defined Network
Kaynak
2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
WoS Q Değeri
Scopus Q Değeri
N/A
