Ateeyah, E.Seker, C.2024-09-292024-09-292023979-835034035-8https://doi.org/10.1109/MysuruCon59703.2023.10397037https://hdl.handle.net/20.500.14619/93033rd IEEE Mysore Sub Section International Conference, MysuruCon 2023 -- 1 December 2023 through 2 December 2023 -- Hassan -- 196843In 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.eninfo:eu-repo/semantics/closedAccessArtificial Neural NetworkBinary multi-neighborhood artificial bee colony AlgorithmDistributed denial-of-serviceSoftware-Defined NetworkConference Object10.1109/MysuruCon59703.2023.103970372-s2.0-85184823441N/A