Machine Learning Approaches in Detecting Network Attacks

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dc.contributor.authorDalmaz, Hasan
dc.contributor.authorErdal, Erdal
dc.contributor.authorÜnver, Halil Murat
dc.date.accessioned2025-01-21T16:26:37Z
dc.date.available2025-01-21T16:26:37Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826
dc.description.abstractDeveloping technology brings many risk in terms of data security. In this regard, it is an important issue to detect attacks for network security. Intrusion detection systems developed due to technological developlments and increasing attack diversity have revealed the necessity of being more succesful in detecting attacks. Today, many studies are carried out on this subject. When the literature is examined, there are various studies with varying success rates in detecting network attacks using machine learning approaches. In this study, the NSL-KDD dataset was explained in detail, the positive aspects of the KDD Cup 99 dataset were specified, the classifier used, performance criteria and the success results obtained were evaluated. In addition, the developed GWO-MFO hybrid algorithm is mentioned and the result is shared. © 2021 IEEE
dc.identifier.doi10.1109/UBMK52708.2021.9558930
dc.identifier.endpage527
dc.identifier.isbn978-166542908-5
dc.identifier.scopus2-s2.0-85125879577
dc.identifier.scopusqualityN/A
dc.identifier.startpage522
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558930
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23148
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectClassifier; Machine learning; Network attacks; NSL-KDD; Performance evaluation
dc.titleMachine Learning Approaches in Detecting Network Attacks
dc.typeConference Object

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