A New Feature Selection Approach and Classification Technique for Current Intrusion Detection System

Basit Öğe Kaydı
dc.contributor.authorOzkan-Okay, Merve
dc.contributor.authorSamet, Refik
dc.contributor.authorAsian, Omer
dc.date.accessioned2024-12-24T19:09:45Z
dc.date.available2024-12-24T19:09:45Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 176826
dc.description.abstractThese days, various devices including computers, smartphones, internet of things (IoT), and cloud services are using computer networks for data communications. As the computer network is being used extensively, it becomes the target of many attacks. It can be different attacks such as denial of service attack (DoS), remote to user attack (R2L), user to remote attack (U2R), and probing attack. To protect communication networks from network-based attacks, intrusion detection systems (IDSs) have been proposed in many studies. However, today IDSs are not good enough to detect new attack types in the communication networks. To increase the efficiency of the current IDSs, a subset of features needs to be obtained before performing the machine learning classifiers. In this study, a new feature selection method is proposed for current IDSs. In addition, the proposed method is combined with machine learning classifiers and tested on KDD '99 dataset and %99.81 accuracy rate was obtained. The obtained performance is pretty high to separate network attacks from the normal traffic. © 2021 IEEE
dc.identifier.doi10.1109/UBMK52708.2021.9559011
dc.identifier.endpage232
dc.identifier.isbn978-166542908-5
dc.identifier.scopus2-s2.0-85125871940
dc.identifier.scopusqualityN/A
dc.identifier.startpage227
dc.identifier.urihttps://doi.org10.1109/UBMK52708.2021.9559011
dc.identifier.urihttps://hdl.handle.net/20.500.12604/3746
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_20241222
dc.subjectAttacks detection
dc.subjectFeature engineering
dc.subjectFeature selection
dc.subjectIntrusion detection
dc.titleA New Feature Selection Approach and Classification Technique for Current Intrusion Detection System
dc.typeConference Object

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu