Detection of Network Anomalies with Machine Learning Methods
| dc.contributor.author | Kara, I.R. | |
| dc.contributor.author | Varol, A. | |
| dc.date.accessioned | 2024-07-12T21:40:42Z | |
| dc.date.available | 2024-07-12T21:40:42Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | [Belirlenecek] | en_US |
| dc.description | IEEE Society | en_US |
| dc.description | 10th International Symposium on Digital Forensics and Security, ISDFS 2022 -- 6 June 2022 through 7 June 2022 -- -- 180285 | en_US |
| dc.description.abstract | The 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. | en_US |
| dc.identifier.doi | 10.1109/ISDFS55398.2022.9800814 | |
| dc.identifier.isbn | 9.78167E+12 | |
| dc.identifier.scopus | 2-s2.0-85134211358 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/ISDFS55398.2022.9800814 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12415/7447 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | 10th International Symposium on Digital Forensics and Security, ISDFS 2022 | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.snmz | KY08791 | |
| dc.subject | Cyber Attack Detection | en_US |
| dc.subject | K-Nearest Neighbor Algorithm | en_US |
| dc.subject | Naive Bayes Theorem | en_US |
| dc.subject | Supervised Learning | en_US |
| dc.subject | Unsw-Nb15 Dataset | en_US |
| dc.title | Detection of Network Anomalies with Machine Learning Methods | en_US |
| dc.type | Conference Object | |
| dspace.entity.type | Publication |
