Öney,M.U.Peker,S.Software Engineering2024-07-052024-07-05201910978-153866878-810.1109/IDAP.2018.8620746https://doi.org/10.1109/IDAP.2018.8620746https://hdl.handle.net/20.500.14411/3929Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs. © 2018 IEEE.eninfo:eu-repo/semantics/closedAccessANNsLiterature ReviewNetwork Intrusion DetectionNeural NetworksSystematic MappingConference Object