Oney, Mehmet UgurPeker, SerhatSoftware Engineering2024-07-052024-07-0520200978303036178597830303617782367-451210.1007/978-3-030-36178-5_492-s2.0-85083459839https://doi.org/10.1007/978-3-030-36178-5_49https://hdl.handle.net/20.500.14411/3444Peker, Serhat/0000-0002-6876-3982Nowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.eninfo:eu-repo/semantics/closedAccessNetwork intrusion detectionData miningData classificationMachine learningANNsConference ObjectQ443597608WOS:000678771000049