Kara, I.R.Varol, A.2024-07-122024-07-1220229.78167E+1210.1109/ISDFS55398.2022.98008142-s2.0-85134211358https://doi.org/10.1109/ISDFS55398.2022.9800814https://hdl.handle.net/20.500.12415/7447IEEE Society10th International Symposium on Digital Forensics and Security, ISDFS 2022 -- 6 June 2022 through 7 June 2022 -- -- 180285The present study, aimed to detect cyber-attacks, and unexpected access requests on devices in the telecommunication networks, enabling the necessary measures to be taken early. With K-Nearest Neighbors (KNN) and Naive Bayes machine learning methods, predicted whether the raw data packets contain cyber-attack according to different properties of these packets using the UNSW-NB15 dataset. KNN algorithms with different K values and the Naive Bayes method were compared according to accuracy rates and the results were given in the table. As a result, changes in accuracy rates were observed according to different k neighbor values in the KNN algorithm. Higher accuracy rates than Naive Bayes were achieved in the models created with the KNN algorithm. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessCyber Attack DetectionK-Nearest Neighbor AlgorithmNaive Bayes TheoremSupervised LearningUnsw-Nb15 DatasetConference ObjectN/A