Detection of network anomalies with machine learning methods
| dc.authorid | 0000-0002-8155-4608 | en_US |
| dc.authorid | 0000-0003-1606-4079 | en_US |
| dc.contributor.author | Kara, İhsan Rıza | |
| dc.contributor.author | Varol, Asaf | |
| dc.date.accessioned | 2024-07-12T20:58:07Z | |
| dc.date.available | 2024-07-12T20:58:07Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | 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. | en_US |
| dc.identifier.citation | Kara, İ.R. and Varol, A. (2022). Detection of network anomalies with machine learning methods. 10th International Symposium on Digital Forensics and Security (ISDFS), s.1-6. | en_US |
| dc.identifier.endpage | 6 | en_US |
| dc.identifier.isbn | 9.78167E+12 | |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12415/3141 | |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | 10th International Symposium on Digital Forensics and Security (ISDFS) | en_US |
| dc.relation.isversionof | 10.1109/ISDFS55398 | en_US |
| dc.relation.publicationcategory | Uluslararası Konferans Öğesi | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.snmz | KY06087 | |
| dc.subject | Cyber attack detection | en_US |
| dc.subject | Supervised learning | en_US |
| dc.subject | KNearest neighbor algorithm | en_US |
| dc.subject | Naive bayes theorem | en_US |
| dc.subject | UNSWNB15 dataset | en_US |
| dc.title | Detection of network anomalies with machine learning methods | en_US |
| dc.type | Conference Object | |
| dspace.entity.type | Publication |
